This document describes the analysis of TCR repertoires for the manuscript “Unique roles of coreceptor-bound LCK in helper and cytotoxic T cells” by Horkova et al. The analysis workflow included:
Raw data are deposited in the Sequence Read Archive (PRJNA872031). Processed files can be downloaded from Zenodo. The Zenodo archive contains the following files, which are needed to run this script:
merged_TCR_repertoires.csvmetadata_Lck.csvcount_table_tra.csv,
count_table_trb.csvimmdata_tra.rds, immdata_trb.rdsLet’s read the required files:
#
merged_repertoires <- read.csv("merged_TCR_repertoires.csv")
md <- read.csv("metadata_Lck.csv")We will start with processing of the file
merged_TCR_repertoires.csv. This file contains outputs of
MiXCR software, i.e., all the assembled CDR3 clonotypes, their counts in
each sample, nucleotide and amino-acid sequences, all V, D and J
alignments with scores, together with some meta-data. the column
num_id contains info about the sample number, which we will
frequently use to attach meta-data to sample numbers.
Here, we summarize the variables in the file
merged_TCR_repertoires.csv:
kable(ExpData(data=merged_repertoires, type=2)) %>%
kable_styling(full_width = F, font_size = 11,
bootstrap_options = c("striped", "hover", "condensed", "responsive"))| Index | Variable_Name | Variable_Type | Sample_n | Missing_Count | Per_of_Missing | No_of_distinct_values |
|---|---|---|---|---|---|---|
| 1 | cloneId | integer | 1001983 | 0 | 0.000 | 68138 |
| 2 | cloneCount | integer | 1001983 | 0 | 0.000 | 120 |
| 3 | cloneFraction | numeric | 1001983 | 0 | 0.000 | 994 |
| 4 | targetSequences | character | 1001983 | 0 | 0.000 | 823904 |
| 5 | targetQualities | character | 1001983 | 0 | 0.000 | 746125 |
| 6 | allVHitsWithScore | character | 1001983 | 0 | 0.000 | 93452 |
| 7 | allDHitsWithScore | character | 566670 | 435313 | 0.434 | 327 |
| 8 | allJHitsWithScore | character | 1001983 | 0 | 0.000 | 6929 |
| 9 | allCHitsWithScore | character | 955 | 1001028 | 0.999 | 122 |
| 10 | allVAlignments | character | 1001983 | 0 | 0.000 | 14932 |
| 11 | allDAlignments | character | 566670 | 435313 | 0.434 | 13102 |
| 12 | allJAlignments | character | 1001983 | 0 | 0.000 | 17988 |
| 13 | allCAlignments | character | 144 | 1001839 | 1.000 | 1 |
| 14 | nSeqCDR3 | character | 1001983 | 0 | 0.000 | 823904 |
| 15 | minQualCDR3 | integer | 1001983 | 0 | 0.000 | 70 |
| 16 | nSeqFR4 | logical | 0 | 1001983 | 1.000 | 0 |
| 17 | aaSeqCDR3 | character | 1001983 | 0 | 0.000 | 541554 |
| 18 | num_id | integer | 1001983 | 0 | 0.000 | 28 |
| 19 | chain | character | 1001983 | 0 | 0.000 | 2 |
| 20 | Mouse_strain | character | 1001983 | 0 | 0.000 | 3 |
| 21 | Cell_type | character | 1001983 | 0 | 0.000 | 2 |
| 22 | Organ | character | 1001983 | 0 | 0.000 | 2 |
| 23 | Cell_count | numeric | 1001983 | 0 | 0.000 | 28 |
| 24 | Exp | integer | 1001983 | 0 | 0.000 | 3 |
| 25 | INDEX_i7 | integer | 1001983 | 0 | 0.000 | 12 |
| 26 | INDEX_i5 | integer | 1001983 | 0 | 0.000 | 3 |
First, we will generate summary count tables for TRA and TRB CDR3
sequences (these tables are provided as Supplementary table S1 in the
manuscript). We will reorder the file so that there will be one column
for each sample, and we will change the num_id of the
samples to meaningful names.
count_table_tra <- merged_repertoires %>%
dplyr::filter(chain == "TRA") %>%
dplyr::select(aaSeqCDR3, num_id, cloneCount)
counts_tra <- count_table_tra %>%
pivot_wider(names_from = num_id, values_from = cloneCount, values_fill = 0, values_fn = sum)
md2_tra <- merged_repertoires %>% filter(chain == "TRA") %>% group_by(aaSeqCDR3) %>% slice_head(n = 1)
excel_count_table_tra <- counts_tra %>% left_join(md2_tra %>% select(allVHitsWithScore, allDHitsWithScore, allJHitsWithScore))
md_to_join <- md %>% mutate(new_name = paste(Cell_type, Organ, Mouse_strain, paste0("Exp0",Exp))) %>% select(num_id, new_name) %>% arrange(num_id)
order <- match(as.numeric(colnames(counts_tra)[2:29]), md_to_join$num_id)
colnames(excel_count_table_tra)[2:29] <- pull(md_to_join, new_name)[order]
excel_count_table_tra2 <- excel_count_table_tra %>% dplyr::select(1, 31, 32, 30, 22, 12, 25, 11, 6, 26, 13, 18, 4, 28, 17, 14, 8, 23, 9, 7, 16, 24, 2, 21, 5, 29, 10, 20, 19, 3, 15, 27)Here, we show the resulting table for TRA:
kable(ExpData(data=excel_count_table_tra2, type=2)) %>%
kable_styling(full_width = F, font_size = 11,
bootstrap_options = c("striped", "hover", "condensed", "responsive"))| Index | Variable_Name | Variable_Type | Sample_n | Missing_Count | Per_of_Missing | No_of_distinct_values |
|---|---|---|---|---|---|---|
| 1 | aaSeqCDR3 | character | 114760 | 0 | 0.000 | 114760 |
| 2 | allDHitsWithScore | character | 4769 | 109991 | 0.958 | 110 |
| 3 | allJHitsWithScore | character | 114760 | 0 | 0.000 | 2980 |
| 4 | allVHitsWithScore | character | 114760 | 0 | 0.000 | 31200 |
| 5 | CD4 Lymph nodes CA Exp01 | integer | 114760 | 0 | 0.000 | 28 |
| 6 | CD4 Lymph nodes CA Exp02 | integer | 114760 | 0 | 0.000 | 22 |
| 7 | CD4 Lymph nodes CA Exp03 | integer | 114760 | 0 | 0.000 | 37 |
| 8 | CD4 Lymph nodes CAKR Exp02 | integer | 114760 | 0 | 0.000 | 26 |
| 9 | CD4 Lymph nodes CAKR Exp03 | integer | 114760 | 0 | 0.000 | 35 |
| 10 | CD4 Lymph nodes WT Exp02 | integer | 114760 | 0 | 0.000 | 29 |
| 11 | CD4 Lymph nodes WT Exp03 | integer | 114760 | 0 | 0.000 | 35 |
| 12 | CD4 Thymus CA Exp01 | integer | 114760 | 0 | 0.000 | 15 |
| 13 | CD4 Thymus CA Exp03 | integer | 114760 | 0 | 0.000 | 32 |
| 14 | CD4 Thymus CAKR Exp01 | integer | 114760 | 0 | 0.000 | 14 |
| 15 | CD4 Thymus CAKR Exp02 | integer | 114760 | 0 | 0.000 | 14 |
| 16 | CD4 Thymus CAKR Exp03 | integer | 114760 | 0 | 0.000 | 21 |
| 17 | CD4 Thymus WT Exp02 | integer | 114760 | 0 | 0.000 | 31 |
| 18 | CD4 Thymus WT Exp03 | integer | 114760 | 0 | 0.000 | 27 |
| 19 | CD8 Lymph nodes CA Exp02 | integer | 114760 | 0 | 0.000 | 21 |
| 20 | CD8 Lymph nodes CA Exp03 | integer | 114760 | 0 | 0.000 | 23 |
| 21 | CD8 Lymph nodes CAKR Exp02 | integer | 114760 | 0 | 0.000 | 17 |
| 22 | CD8 Lymph nodes CAKR Exp03 | integer | 114760 | 0 | 0.000 | 25 |
| 23 | CD8 Lymph nodes WT Exp01 | integer | 114760 | 0 | 0.000 | 26 |
| 24 | CD8 Lymph nodes WT Exp02 | integer | 114760 | 0 | 0.000 | 31 |
| 25 | CD8 Lymph nodes WT Exp03 | integer | 114760 | 0 | 0.000 | 30 |
| 26 | CD8 Thymus CA Exp01 | integer | 114760 | 0 | 0.000 | 9 |
| 27 | CD8 Thymus CA Exp03 | integer | 114760 | 0 | 0.000 | 23 |
| 28 | CD8 Thymus CAKR Exp02 | integer | 114760 | 0 | 0.000 | 10 |
| 29 | CD8 Thymus CAKR Exp03 | integer | 114760 | 0 | 0.000 | 11 |
| 30 | CD8 Thymus WT Exp01 | integer | 114760 | 0 | 0.000 | 22 |
| 31 | CD8 Thymus WT Exp02 | integer | 114760 | 0 | 0.000 | 9 |
| 32 | CD8 Thymus WT Exp03 | integer | 114760 | 0 | 0.000 | 30 |
count_table_trb <- merged_repertoires %>%
dplyr::filter(chain == "TRB") %>%
dplyr::select(aaSeqCDR3, num_id, cloneCount)
counts_trb <- count_table_trb %>%
pivot_wider(names_from = num_id, values_from = cloneCount, values_fill = 0, values_fn = sum)
md2_trb <- merged_repertoires %>%
filter(chain == "TRB") %>%
group_by(aaSeqCDR3) %>%
slice_head(n = 1)
excel_count_table_trb <- counts_trb %>%
left_join(md2_trb %>%
select(allVHitsWithScore, allDHitsWithScore, allJHitsWithScore))
md_to_join <- md %>%
mutate(new_name = paste(Cell_type, Organ, Mouse_strain, paste0("Exp0",Exp))) %>%
select(num_id, new_name) %>%
arrange(num_id)
order <- match(as.numeric(colnames(counts_trb)[2:29]), md_to_join$num_id)
colnames(excel_count_table_trb)[2:29] <- pull(md_to_join, new_name)[order]
excel_count_table_trb2 <- excel_count_table_trb %>%
dplyr::select(1, 30:32, 22, 12, 25, 11, 6, 26, 13, 18, 4, 28, 17, 14, 8, 23, 9, 7, 16, 24, 2, 21, 5, 29, 10, 20, 19, 3, 15, 27)Here, we show the resulting table for TRB:
kable(ExpData(data=excel_count_table_trb2, type=2)) %>%
kable_styling(full_width = F, font_size = 11,
bootstrap_options = c("striped", "hover", "condensed", "responsive"))| Index | Variable_Name | Variable_Type | Sample_n | Missing_Count | Per_of_Missing | No_of_distinct_values |
|---|---|---|---|---|---|---|
| 1 | aaSeqCDR3 | character | 426800 | 0 | 0.000 | 426800 |
| 2 | allVHitsWithScore | character | 426800 | 0 | 0.000 | 23763 |
| 3 | allDHitsWithScore | character | 331672 | 95128 | 0.223 | 214 |
| 4 | allJHitsWithScore | character | 426800 | 0 | 0.000 | 2168 |
| 5 | CD4 Lymph nodes CA Exp01 | integer | 426800 | 0 | 0.000 | 29 |
| 6 | CD4 Lymph nodes CA Exp02 | integer | 426800 | 0 | 0.000 | 21 |
| 7 | CD4 Lymph nodes CA Exp03 | integer | 426800 | 0 | 0.000 | 33 |
| 8 | CD4 Lymph nodes CAKR Exp02 | integer | 426800 | 0 | 0.000 | 20 |
| 9 | CD4 Lymph nodes CAKR Exp03 | integer | 426800 | 0 | 0.000 | 38 |
| 10 | CD4 Lymph nodes WT Exp02 | integer | 426800 | 0 | 0.000 | 30 |
| 11 | CD4 Lymph nodes WT Exp03 | integer | 426800 | 0 | 0.000 | 25 |
| 12 | CD4 Thymus CA Exp01 | integer | 426800 | 0 | 0.000 | 24 |
| 13 | CD4 Thymus CA Exp03 | integer | 426800 | 0 | 0.000 | 42 |
| 14 | CD4 Thymus CAKR Exp01 | integer | 426800 | 0 | 0.000 | 19 |
| 15 | CD4 Thymus CAKR Exp02 | integer | 426800 | 0 | 0.000 | 14 |
| 16 | CD4 Thymus CAKR Exp03 | integer | 426800 | 0 | 0.000 | 39 |
| 17 | CD4 Thymus WT Exp02 | integer | 426800 | 0 | 0.000 | 17 |
| 18 | CD4 Thymus WT Exp03 | integer | 426800 | 0 | 0.000 | 15 |
| 19 | CD8 Lymph nodes CA Exp02 | integer | 426800 | 0 | 0.000 | 24 |
| 20 | CD8 Lymph nodes CA Exp03 | integer | 426800 | 0 | 0.000 | 25 |
| 21 | CD8 Lymph nodes CAKR Exp02 | integer | 426800 | 0 | 0.000 | 24 |
| 22 | CD8 Lymph nodes CAKR Exp03 | integer | 426800 | 0 | 0.000 | 32 |
| 23 | CD8 Lymph nodes WT Exp01 | integer | 426800 | 0 | 0.000 | 22 |
| 24 | CD8 Lymph nodes WT Exp02 | integer | 426800 | 0 | 0.000 | 32 |
| 25 | CD8 Lymph nodes WT Exp03 | integer | 426800 | 0 | 0.000 | 23 |
| 26 | CD8 Thymus CA Exp01 | integer | 426800 | 0 | 0.000 | 9 |
| 27 | CD8 Thymus CA Exp03 | integer | 426800 | 0 | 0.000 | 20 |
| 28 | CD8 Thymus CAKR Exp02 | integer | 426800 | 0 | 0.000 | 14 |
| 29 | CD8 Thymus CAKR Exp03 | integer | 426800 | 0 | 0.000 | 14 |
| 30 | CD8 Thymus WT Exp01 | integer | 426800 | 0 | 0.000 | 16 |
| 31 | CD8 Thymus WT Exp02 | integer | 426800 | 0 | 0.000 | 9 |
| 32 | CD8 Thymus WT Exp03 | integer | 426800 | 0 | 0.000 | 20 |
In addition to count matrices, we will also need normalized frequency tables, in which we will show the percentage of the repertoire occupied by each CDR3 sequence in a particular sample. We will first just normalize the count matrices we created earlier.
For TRA, we will also remove canonical NKT-cell CDR3 sequences TRAV11-TRAJ18 before normalization.
Here comes the normalized table for TRA:
excel_count_table_tra3 <- excel_count_table_tra2 %>%
mutate(nkt_trav11_traj18 = if_else(
(grepl(allVHitsWithScore, pattern = "TRAV11") &
(grepl(allJHitsWithScore, pattern = "TRAJ18"))),"yes","no"))
excel_count_table_tra4 <- excel_count_table_tra3 %>%
filter(nkt_trav11_traj18 == "no")
count_table_tra4 <- as.matrix(excel_count_table_tra4[,5:32])
rownames(count_table_tra4) <- excel_count_table_tra4$aaSeqCDR3
tra4_norm <- scale(count_table_tra4, center=FALSE, scale=colSums(count_table_tra4))
prop.table.tra <- cbind(tra4_norm,
excel_count_table_tra4 %>%
select(-starts_with("CD")) )
prop.table.tra2 <- prop.table.tra %>%
dplyr::select(29, 31, 30, 32, 1:28)
data.table(prop.table.tra2 %>% head) ## aaSeqCDR3 allJHitsWithScore allDHitsWithScore
## 1: CAASASSGSWQLIF TRAJ22*00(303.6) <NA>
## 2: CAASNMGYKLTF TRAJ9*00(284.4) <NA>
## 3: CAVSASSGSWQLIF TRAJ22*00(304.2) <NA>
## 4: CAASDNYAQGLTF TRAJ26*00(284) <NA>
## 5: CALSDRYNQGKLIF TRAJ23*00(272.3) <NA>
## 6: CAASDDTNAYKVIF TRAJ30*00(295.7) <NA>
## allVHitsWithScore
## 1: TRAV14N-2*00(1026.1),TRAV14D-2*00(1017.8),TRAV14D-1*00(973.4),TRAV14N-1*00(973.4),TRAV14-2*00(944.4)
## 2: TRAV7-2*00(731.5),TRAV7D-2*00(731.5)
## 3: TRAV3D-3*00(975.2),TRAV3N-3*00(975.2),TRAV3-3*00(944.1)
## 4: TRAV14D-1*00(878.2),TRAV14N-1*00(878.2),TRAV14D-2*00(848.2),TRAV14N-2*00(848.2)
## 5: TRAV12D-3*00(1377.3),TRAV12N-3*00(1377.3)
## 6: TRAV14-1*00(966.7)
## CD4 Lymph nodes CA Exp01 CD4 Lymph nodes CA Exp02 CD4 Lymph nodes CA Exp03
## 1: 2.727925e-03 0.0035273369 3.841094e-03
## 2: 1.148600e-03 0.0016166961 1.104972e-03
## 3: 7.178751e-04 0.0007348618 3.157064e-04
## 4: 2.871500e-04 0.0000000000 3.157064e-04
## 5: 7.178751e-05 0.0001469724 4.209419e-04
## 6: 0.000000e+00 0.0000000000 5.261773e-05
## CD4 Lymph nodes CAKR Exp02 CD4 Lymph nodes CAKR Exp03
## 1: 0.0027958993 2.668321e-03
## 2: 0.0013979497 8.687558e-04
## 3: 0.0004659832 5.584859e-04
## 4: 0.0007455732 1.861620e-04
## 5: 0.0000000000 6.205399e-05
## 6: 0.0003727866 1.241080e-04
## CD4 Lymph nodes WT Exp02 CD4 Lymph nodes WT Exp03 CD4 Thymus CA Exp01
## 1: 0.0035501049 3.291020e-03 0.0028328612
## 2: 0.0008068420 1.097007e-03 0.0012876642
## 3: 0.0002420526 2.611921e-04 0.0005150657
## 4: 0.0001613684 1.567152e-04 0.0000000000
## 5: 0.0000806842 5.223842e-05 0.0000000000
## 6: 0.0000000000 1.567152e-04 0.0002575328
## CD4 Thymus CA Exp03 CD4 Thymus CAKR Exp01 CD4 Thymus CAKR Exp02
## 1: 0.0022079929 0.0013199578 0.0037400655
## 2: 0.0013247958 0.0013199578 0.0000000000
## 3: 0.0003679988 0.0000000000 0.0023375409
## 4: 0.0003679988 0.0005279831 0.0000000000
## 5: 0.0000000000 0.0000000000 0.0000000000
## 6: 0.0000000000 0.0000000000 0.0004675082
## CD4 Thymus CAKR Exp03 CD4 Thymus WT Exp02 CD4 Thymus WT Exp03
## 1: 0.0033652602 0.0025633010 2.775089e-03
## 2: 0.0001294331 0.0013754298 5.808325e-04
## 3: 0.0005177323 0.0005001563 3.872217e-04
## 4: 0.0000000000 0.0003125977 3.872217e-04
## 5: 0.0001294331 0.0000000000 6.453695e-05
## 6: 0.0000000000 0.0003751172 1.936108e-04
## CD8 Lymph nodes CA Exp02 CD8 Lymph nodes CA Exp03 CD8 Lymph nodes CAKR Exp02
## 1: 0.0099369387 0.0093881515 0.0080917874
## 2: 0.0018154023 0.0020233085 0.0019323671
## 3: 0.0021975922 0.0018614438 0.0008454106
## 4: 0.0009554749 0.0009711881 0.0007246377
## 5: 0.0011465698 0.0008902557 0.0016908213
## 6: 0.0012421173 0.0008093234 0.0006038647
## CD8 Lymph nodes CAKR Exp03 CD8 Lymph nodes WT Exp01 CD8 Lymph nodes WT Exp02
## 1: 0.0096842588 0.009602698 0.011932808
## 2: 0.0022348289 0.002492303 0.001946123
## 3: 0.0020056157 0.001905879 0.001894909
## 4: 0.0009741562 0.001612667 0.001177917
## 5: 0.0015471893 0.003665152 0.003021612
## 6: 0.0004011231 0.001319455 0.000870634
## CD8 Lymph nodes WT Exp03 CD8 Thymus CA Exp01 CD8 Thymus CA Exp03
## 1: 0.0097454014 0.0062111801 0.0073130455
## 2: 0.0021318066 0.0014614541 0.0025163167
## 3: 0.0019490803 0.0014614541 0.0008649839
## 4: 0.0012790839 0.0010960906 0.0004718094
## 5: 0.0024363503 0.0003653635 0.0005504443
## 6: 0.0007918139 0.0007307271 0.0009436188
## CD8 Thymus CAKR Exp02 CD8 Thymus CAKR Exp03 CD8 Thymus WT Exp01
## 1: 0.0084643289 0.0065963061 0.0088170463
## 2: 0.0008061266 0.0015077271 0.0020992967
## 3: 0.0016122531 0.0009423294 0.0019943319
## 4: 0.0012091898 0.0011307953 0.0015744726
## 5: 0.0004030633 0.0005653977 0.0016794374
## 6: 0.0016122531 0.0016961930 0.0007347539
## CD8 Thymus WT Exp02 CD8 Thymus WT Exp03
## 1: 0.0053846154 0.007650815
## 2: 0.0015384615 0.001761339
## 3: 0.0015384615 0.001541171
## 4: 0.0023076923 0.001431088
## 5: 0.0019230769 0.001155878
## 6: 0.0003846154 0.001265962
Here comes the normalized table for TRA:
excel_count_table_trb3 <- excel_count_table_trb2 %>%
mutate(nkt_trav11_traj18 = "no")
count_table_trb4 <- as.matrix(excel_count_table_trb3[,5:32])
rownames(count_table_trb4) <- excel_count_table_trb3$aaSeqCDR3
trb4_norm <- scale(count_table_trb4, center=FALSE, scale=colSums(count_table_trb4))
prop.table.trb <- cbind(trb4_norm, excel_count_table_trb %>% select(-starts_with("CD")) )
colnames(prop.table.trb)## [1] "CD4 Lymph nodes CA Exp01" "CD4 Lymph nodes CA Exp02"
## [3] "CD4 Lymph nodes CA Exp03" "CD4 Lymph nodes CAKR Exp02"
## [5] "CD4 Lymph nodes CAKR Exp03" "CD4 Lymph nodes WT Exp02"
## [7] "CD4 Lymph nodes WT Exp03" "CD4 Thymus CA Exp01"
## [9] "CD4 Thymus CA Exp03" "CD4 Thymus CAKR Exp01"
## [11] "CD4 Thymus CAKR Exp02" "CD4 Thymus CAKR Exp03"
## [13] "CD4 Thymus WT Exp02" "CD4 Thymus WT Exp03"
## [15] "CD8 Lymph nodes CA Exp02" "CD8 Lymph nodes CA Exp03"
## [17] "CD8 Lymph nodes CAKR Exp02" "CD8 Lymph nodes CAKR Exp03"
## [19] "CD8 Lymph nodes WT Exp01" "CD8 Lymph nodes WT Exp02"
## [21] "CD8 Lymph nodes WT Exp03" "CD8 Thymus CA Exp01"
## [23] "CD8 Thymus CA Exp03" "CD8 Thymus CAKR Exp02"
## [25] "CD8 Thymus CAKR Exp03" "CD8 Thymus WT Exp01"
## [27] "CD8 Thymus WT Exp02" "CD8 Thymus WT Exp03"
## [29] "aaSeqCDR3" "allVHitsWithScore"
## [31] "allDHitsWithScore" "allJHitsWithScore"
prop.table.trb2 <- prop.table.trb %>% dplyr::select(29, 31, 30, 32, 1:28)
data.table(prop.table.trb2 %>% head)## aaSeqCDR3 allDHitsWithScore allVHitsWithScore
## 1: CASSDSAETLYF <NA> TRBV13-3*00(755.1),TRBV13-1*00(664.5)
## 2: CASSDAEQFF <NA> TRBV13-1*00(819.6)
## 3: CASSDAGYEQYF <NA> TRBV13-1*00(1283.8)
## 4: CASSDWGGYAEQFF TRBD2*00(45) TRBV13-1*00(676)
## 5: CASSDSGGQDTQYF TRBD2*00(35) TRBV13-3*00(886.3)
## 6: CASRDRNTEVFF TRBD1*00(30) TRBV13-3*00(979)
## allJHitsWithScore CD4 Lymph nodes CA Exp01 CD4 Lymph nodes CA Exp02
## 1: TRBJ2-3*00(245) 1.729847e-04 5.154108e-05
## 2: TRBJ2-1*00(219.2) 0.000000e+00 0.000000e+00
## 3: TRBJ2-7*00(215) 0.000000e+00 5.154108e-05
## 4: TRBJ2-1*00(235) 7.413631e-05 5.154108e-05
## 5: TRBJ2-5*00(235) 2.471210e-05 0.000000e+00
## 6: TRBJ1-1*00(232.7) 1.482726e-04 5.154108e-05
## CD4 Lymph nodes CA Exp03 CD4 Lymph nodes CAKR Exp02
## 1: 1.141010e-04 3.152287e-05
## 2: 9.508415e-05 0.000000e+00
## 3: 3.803366e-05 0.000000e+00
## 4: 1.901683e-05 1.260915e-04
## 5: 3.803366e-05 3.152287e-05
## 6: 5.705049e-05 9.456861e-05
## CD4 Lymph nodes CAKR Exp03 CD4 Lymph nodes WT Exp02 CD4 Lymph nodes WT Exp03
## 1: 2.747190e-04 2.637618e-05 2.293368e-04
## 2: 2.289325e-05 5.275235e-05 2.293368e-05
## 3: 6.867975e-05 2.637618e-04 2.293368e-05
## 4: 0.000000e+00 0.000000e+00 0.000000e+00
## 5: 0.000000e+00 0.000000e+00 0.000000e+00
## 6: 0.000000e+00 0.000000e+00 6.880103e-05
## CD4 Thymus CA Exp01 CD4 Thymus CA Exp03 CD4 Thymus CAKR Exp01
## 1: 2.366397e-04 6.682259e-05 8.731718e-05
## 2: 7.887991e-05 4.454839e-05 0.000000e+00
## 3: 3.549596e-04 1.113710e-04 4.365859e-05
## 4: 1.577598e-04 2.227420e-05 0.000000e+00
## 5: 0.000000e+00 0.000000e+00 0.000000e+00
## 6: 0.000000e+00 4.454839e-05 0.000000e+00
## CD4 Thymus CAKR Exp02 CD4 Thymus CAKR Exp03 CD4 Thymus WT Exp02
## 1: 0 0.0000000000 4.972527e-05
## 2: 0 0.0000000000 2.486263e-05
## 3: 0 0.0000300075 2.486263e-05
## 4: 0 0.0000000000 2.486263e-05
## 5: 0 0.0000000000 2.486263e-05
## 6: 0 0.0002100525 0.000000e+00
## CD4 Thymus WT Exp03 CD8 Lymph nodes CA Exp02 CD8 Lymph nodes CA Exp03
## 1: 2.538554e-04 3.846495e-04 3.699984e-04
## 2: 3.173193e-05 2.662958e-04 1.849992e-04
## 3: 0.000000e+00 3.254727e-04 2.642846e-04
## 4: 3.173193e-05 1.183537e-04 2.642846e-04
## 5: 0.000000e+00 5.917685e-05 5.285692e-05
## 6: 3.173193e-05 2.367074e-04 5.285692e-05
## CD8 Lymph nodes CAKR Exp02 CD8 Lymph nodes CAKR Exp03
## 1: 3.195909e-04 2.544113e-04
## 2: 1.452686e-04 7.268895e-05
## 3: 3.776984e-04 2.544113e-04
## 4: 9.878265e-04 4.179614e-04
## 5: 2.905372e-05 7.268895e-05
## 6: 1.452686e-04 1.272057e-04
## CD8 Lymph nodes WT Exp01 CD8 Lymph nodes WT Exp02 CD8 Lymph nodes WT Exp03
## 1: 0.0005254621 3.488707e-04 0.0002286917
## 2: 0.0002741541 1.365146e-04 0.0001829533
## 3: 0.0005483082 6.219000e-04 0.0005031216
## 4: 0.0003198465 2.730293e-04 0.0002058225
## 5: 0.0003198465 4.550488e-05 0.0000000000
## 6: 0.0002056156 4.550488e-05 0.0001372150
## CD8 Thymus CA Exp01 CD8 Thymus CA Exp03 CD8 Thymus CAKR Exp02
## 1: 0.0003316475 1.856493e-04 0.0000000000
## 2: 0.0002487356 9.282465e-05 0.0002597628
## 3: 0.0003316475 1.856493e-04 0.0000000000
## 4: 0.0001658237 3.094155e-05 0.0003463503
## 5: 0.0000000000 3.094155e-05 0.0000000000
## 6: 0.0000000000 1.237662e-04 0.0000000000
## CD8 Thymus CAKR Exp03 CD8 Thymus WT Exp01 CD8 Thymus WT Exp02
## 1: 0.0000000000 1.804457e-04 4.479484e-04
## 2: 0.0002214839 3.308171e-04 2.687690e-04
## 3: 0.0001661130 4.210400e-04 3.583587e-04
## 4: 0.0002214839 2.706686e-04 2.687690e-04
## 5: 0.0001107420 6.014857e-05 0.000000e+00
## 6: 0.0000000000 1.202971e-04 8.958968e-05
## CD8 Thymus WT Exp03
## 1: 2.347268e-04
## 2: 8.535518e-05
## 3: 2.774043e-04
## 4: 6.401639e-05
## 5: 2.133880e-05
## 6: 1.066940e-04
To compare the gene segments used by T-cells from different mouse
strains, we will use the R package Immunarch. First, we
loaded the MiXCR outputs using the repLoad function and
saved it as immdata_tra and immdata_trb,
respectively to facilitate the analysis. We will load the prepared
datasets:
immdata_tra <- readRDS("immdata_tra.rds")
immdata_trb <- readRDS("immdata_trb.rds")Using the gene usage visualization from the Immunarch
package, we will plot the gene segments of TRA chains and TRB chains for
each cell type and each organ.
### Thymus
cd4_thymus <- geneUsage(repFilter(immdata_tra,
.method = "by.meta",
.query = list(Organ = include('Thymus'), Cell_type = include('CD4')))$data, .norm = T, .ambig = "maj", .quant = "count", .gene = "musmus.trav")
vis(cd4_thymus, .by = c("Cell_type", "Organ","Mouse_strain"), .meta = immdata_tra$meta) + ggtitle("TRA Thymus CD4") + scale_fill_manual(values = c("dodgerblue","indianred2","gray70"))# source data
write.csv(cd4_thymus, "source_data/FigED7_cd4_thymus_tra.csv")
#ggsave("./final_fig/gene_usage_new/cd4_thymus_tra_just1segment.png", width = 60, height = 12, units = "cm")
#ggsave("./final_fig/gene_usage_new/cd4_thymus_tra_just1segment.svg", width = 60, height = 12, units = "cm")### LN
cd4_ln <- geneUsage(repFilter(immdata_tra,
.method = "by.meta",
.query = list(Organ = exclude('Thymus'), Cell_type = include('CD4')))$data, .norm = T, .ambig = "maj", .quant = "count", .gene = "musmus.trav")
vis(cd4_ln, .by = c("Cell_type", "Organ","Mouse_strain"), .meta = immdata_tra$meta) + ggtitle("TRA LN CD4") + scale_fill_manual(values = c("dodgerblue","indianred2","gray70"))# source data
write.csv(cd4_ln, "source_data/FigED7_cd4_ln_tra.csv")
#ggsave("./final_fig/gene_usage_new/cd4_ln_tra_just1segment.png", width = 60, height = 12, units = "cm")
#ggsave("./final_fig/gene_usage_new/cd4_ln_tra_just1segment.svg", width = 60, height = 12, units = "cm")cd8_thymus <- geneUsage(repFilter(immdata_tra,
.method = "by.meta",
.query = list(Organ = include('Thymus'), Cell_type = include('CD8')))$data, .norm = T, .ambig = "maj", .quant = "count", .gene = "musmus.trav")
vis(cd8_thymus, .by = c("Cell_type", "Organ","Mouse_strain"), .meta = immdata_tra$meta) + ggtitle("TRA Thymus CD8") + scale_fill_manual(values = c("dodgerblue","indianred2","gray70"))# source data
write.csv(cd8_thymus, "source_data/FigED7_cd8_thymus_tra.csv")
#ggsave("./final_fig/gene_usage_new/cd8_thymus_tra_just1segment.png", width = 60, height = 12, units = "cm")
#ggsave("./final_fig/gene_usage_new/cd8_thymus_tra_just1segment.svg", width = 60, height = 12, units = "cm")cd8_ln <- geneUsage(repFilter(immdata_tra,
.method = "by.meta",
.query = list(Organ = exclude('Thymus'), Cell_type = include('CD8')))$data, .norm = T, .ambig = "maj", .quant = "count", .gene = "musmus.trav")
vis(cd8_ln, .by = c("Cell_type", "Organ","Mouse_strain"), .meta = immdata_tra$meta) + ggtitle("TRA LN CD8") + scale_fill_manual(values = c("dodgerblue","indianred2","gray70"))# source data
write.csv(cd8_ln, "source_data/FigED7_cd8_ln_tra.csv")
#ggsave("./final_fig/gene_usage_new/cd8_ln_tra_just1segment.png", width = 60, height = 12, units = "cm")
#ggsave("./final_fig/gene_usage_new/cd8_ln_tra_just1segment.svg", width = 60, height = 12, units = "cm")### Thymus
cd4_thymus <- geneUsage(repFilter(immdata_trb,
.method = "by.meta",
.query = list(Organ = include('Thymus'), Cell_type = include('CD4')))$data, .norm = T, .ambig = "maj", .quant = "count", .gene = "musmus.trbv")
vis(cd4_thymus, .by = c("Cell_type", "Organ","Mouse_strain"), .meta = immdata_trb$meta, .test = F) + ggtitle("TRB Thymus CD4") + scale_fill_manual(values = c("dodgerblue","indianred2","gray70")) # source data
write.csv(cd4_thymus, "source_data/FigED7_cd4_thymus_trb.csv")
#ggsave("./final_fig/gene_usage_new/cd4_thymus_trb_just1segment.png", width = 20, height = 10, units = "cm")
#ggsave("./final_fig/gene_usage_new/cd4_thymus_trb_just1segment.svg", width = 20, height = 10, units = "cm")### LN
cd4_ln <- geneUsage(repFilter(immdata_trb,
.method = "by.meta",
.query = list(Organ = exclude('Thymus'), Cell_type = include('CD4')))$data, .norm = T, .ambig = "maj", .quant = "count", .gene = "musmus.trbv")
vis(cd4_ln, .by = c("Cell_type", "Organ","Mouse_strain"), .meta = immdata_trb$meta, .test = F) + ggtitle("TRB LN CD4") + scale_fill_manual(values = c("dodgerblue","indianred2","gray70"))# source data
write.csv(cd4_ln, "source_data/FigED7_cd4_ln_trb.csv")
#ggsave("./final_fig/gene_usage_new/cd4_ln_trb_just1segment.png", width = 20, height = 10, units = "cm")
#ggsave("./final_fig/gene_usage_new/cd4_ln_trb_just1segment.svg", width = 20, height = 10, units = "cm")cd8_thymus <- geneUsage(repFilter(immdata_trb,
.method = "by.meta",
.query = list(Organ = include('Thymus'), Cell_type = include('CD8')))$data, .norm = T, .ambig = "maj", .quant = "count", .gene = "musmus.trbv")
vis(cd8_thymus, .by = c("Cell_type", "Organ","Mouse_strain"), .meta = immdata_trb$meta, .test = F) + ggtitle("TRB Thymus CD8") + scale_fill_manual(values = c("dodgerblue","indianred2","gray70"))# source data
write.csv(cd8_thymus, "source_data/FigED7_cd8_thymus_trb.csv")
#ggsave("./final_fig/gene_usage_new/cd8_thymus_trb_just1segment.png", width = 20, height = 10, units = "cm")
#ggsave("./final_fig/gene_usage_new/cd8_thymus_trb_just1segment.svg", width = 20, height = 10, units = "cm")cd8_ln <- geneUsage(repFilter(immdata_trb,
.method = "by.meta",
.query = list(Organ = exclude('Thymus'), Cell_type = include('CD8')))$data, .norm = T, .ambig = "maj", .quant = "count", .gene = "musmus.trbv")
vis(cd8_ln, .by = c("Cell_type", "Organ","Mouse_strain"), .meta = immdata_trb$meta, .test = F) + ggtitle("TRB LN CD8") + scale_fill_manual(values = c("dodgerblue","indianred2","gray70"))# source data
write.csv(cd8_ln, "source_data/FigED7_cd8_ln_trb.csv")
#ggsave("./final_fig/gene_usage_new/cd8_ln_trb_just1segment.png", width = 20, height = 10, units = "cm")
#ggsave("./final_fig/gene_usage_new/cd8_ln_trb_just1segment.svg", width = 20, height = 10, units = "cm")Next, we will identify gene segments that are typical for invariant NKT cells. These segments are TRAV11 and TRAJ18 for TRA and TRBV1, TRBV13-2 and TRBV29 for TRB. Please, note that some of the included TRB chains are used by conventional cells as well.
# NKT analysis
merged_repertoires <- merged_repertoires %>%
mutate(is_nkt = if_else(
(grepl(allVHitsWithScore, pattern = "TRAV11")) & (grepl(allJHitsWithScore, pattern = "TRAJ18")) |
(grepl(allVHitsWithScore, pattern = "TRBV13-2")) |
(grepl(allVHitsWithScore, pattern = "TRBV1\\*")) |
(grepl(allVHitsWithScore, pattern = "TRBV29")) ,"yes","no")) %>%
mutate(nkt_trav11_traj18 = if_else(
(grepl(allVHitsWithScore, pattern = "TRAV11") & (grepl(allJHitsWithScore, pattern = "TRAJ18"))),"yes","no")) %>% mutate(nkt_trbv13_2 = if_else(
(grepl(allVHitsWithScore, pattern = "TRBV13-2")),"yes","no")) %>% mutate(nkt_trbv29 = if_else(
(grepl(allVHitsWithScore, pattern = "TRBV29")),"yes","no")) %>% mutate(nkt_trbv1 = if_else(
(grepl(allVHitsWithScore, pattern = "TRBV1\\*")),"yes","no"))
md <- md %>% select(Exp, Organ, Cell_type, Mouse_strain, num_id) %>% mutate_at(vars("num_id"), as.numeric)
# All TRB NKT
nkt_table_trb <- merged_repertoires %>%
filter(chain == "TRB") %>%
mutate(new_name = paste(Exp, Organ, Cell_type, Mouse_strain)) %>%
dplyr::select(new_name, cloneCount, is_nkt, num_id) %>%
uncount(cloneCount) %>%
group_by(num_id, is_nkt) %>%
summarise(n = n()) %>%
mutate(freq = n / sum(n)) %>% dplyr::filter(is_nkt == "yes") %>%
ungroup %>%
left_join(md) %>%
unique %>%
mutate(sample_type = paste(Cell_type, Organ, Mouse_strain))
# TRAV11 TRAJ18
nkt_table_trav11_traj18 <- merged_repertoires %>%
filter(chain == "TRA") %>%
mutate(new_name = paste(Exp, Organ, Cell_type, Mouse_strain)) %>%
dplyr::select(new_name, cloneCount, nkt_trav11_traj18, num_id) %>%
uncount(cloneCount) %>%
group_by(num_id, nkt_trav11_traj18) %>%
summarise(n = n()) %>%
mutate(freq = n / sum(n)) %>% dplyr::filter(nkt_trav11_traj18 == "yes") %>%
ungroup %>%
left_join(md) %>%
unique %>%
mutate(sample_type = paste(Cell_type, Organ, Mouse_strain))
# TRBV1
nkt_table_trbv1 <- merged_repertoires %>%
filter(chain == "TRB") %>%
mutate(new_name = paste(Exp, Organ, Cell_type, Mouse_strain)) %>%
dplyr::select(new_name, cloneCount, nkt_trbv1, num_id) %>%
uncount(cloneCount) %>%
group_by(num_id, nkt_trbv1) %>%
summarise(n = n()) %>%
mutate(freq = n / sum(n)) %>% dplyr::filter(nkt_trbv1 == "yes") %>%
ungroup %>%
left_join(md) %>%
unique %>%
mutate(sample_type = paste(Cell_type, Organ, Mouse_strain))
# TRBV13-2
nkt_table_trbv13_2 <- merged_repertoires %>%
filter(chain == "TRB") %>%
mutate(new_name = paste(Exp, Organ, Cell_type, Mouse_strain)) %>%
dplyr::select(new_name, cloneCount, nkt_trbv13_2, num_id) %>%
uncount(cloneCount) %>%
group_by(num_id, nkt_trbv13_2) %>%
summarise(n = n()) %>%
mutate(freq = n / sum(n)) %>% dplyr::filter(nkt_trbv13_2 == "yes") %>%
ungroup %>%
left_join(md) %>%
unique %>%
mutate(sample_type = paste(Cell_type, Organ, Mouse_strain))
# TRBV29
nkt_table_trbv29 <- merged_repertoires %>%
filter(chain == "TRB") %>%
mutate(new_name = paste(Exp, Organ, Cell_type, Mouse_strain)) %>%
dplyr::select(new_name, cloneCount, nkt_trbv29, num_id) %>%
uncount(cloneCount) %>%
group_by(num_id, nkt_trbv29) %>%
summarise(n = n()) %>%
mutate(freq = n / sum(n)) %>% dplyr::filter(nkt_trbv29 == "yes") %>%
ungroup %>%
left_join(md) %>%
unique %>%
mutate(sample_type = paste(Cell_type, Organ, Mouse_strain))
levels_cd4 <- c("CD4 Thymus WT", "CD4 Thymus CA", "CD4 Thymus CAKR", "CD4 Lymph nodes WT", "CD4 Lymph nodes CA", "CD4 Lymph nodes CAKR")
levels_cd8 <- c("CD8 Thymus WT", "CD8 Thymus CA", "CD8 Thymus CAKR", "CD8 Lymph nodes WT", "CD8 Lymph nodes CA", "CD8 Lymph nodes CAKR")
nkt_table_trb$nkt_sample <- "all_nkt_trb"
nkt_table_trav11_traj18$nkt_sample <- "trav11_traj18"
nkt_table_trbv1$nkt_sample <- "trbv1"
nkt_table_trbv13_2$nkt_sample <- "trbv13_2"
nkt_table_trbv29$nkt_sample <- "trbv29"
nkt_table <- cbind(nkt_table_trb$num_id, nkt_table_trb$sample_type, nkt_table_trb$Exp, format(nkt_table_trb$freq*100, digits = 2),
format(nkt_table_trav11_traj18$freq*100, digits = 2),
format(nkt_table_trbv1$freq*100, digits = 2),
format(nkt_table_trbv13_2$freq*100, digits = 2),
format(nkt_table_trbv29$freq*100, digits = 2)) %>% as.data.frame
colnames(nkt_table) <- c("num_id","Sample","Exp","All TRB NKT", "TRAV11-TRAJ18", "TRBV1","TRBV13-2","TRBV29")
nkt_table <- nkt_table %>% mutate(Sample = paste(Sample, Exp)) %>% select(-num_id, -Exp) %>% arrange(Sample)See the percentage of NKT gene segments in each sample:
kable(nkt_table, format = "html") %>%
kable_styling(full_width = F, font_size = 11,
bootstrap_options = c("striped", "hover", "condensed", "responsive"))| Sample | All TRB NKT | TRAV11-TRAJ18 | TRBV1 | TRBV13-2 | TRBV29 |
|---|---|---|---|---|---|
| CD4 Lymph nodes CA 1 | 18 | 2.574 | 3.2 | 12.0 | 2.7 |
| CD4 Lymph nodes CA 2 | 19 | 2.326 | 3.1 | 12.4 | 3.1 |
| CD4 Lymph nodes CA 3 | 16 | 2.046 | 2.4 | 10.9 | 2.8 |
| CD4 Lymph nodes CAKR 2 | 19 | 1.704 | 3.1 | 12.7 | 2.9 |
| CD4 Lymph nodes CAKR 3 | 18 | 2.357 | 3.4 | 12.0 | 3.0 |
| CD4 Lymph nodes WT 2 | 17 | 0.474 | 2.8 | 11.4 | 2.9 |
| CD4 Lymph nodes WT 3 | 17 | 0.307 | 3.1 | 11.1 | 2.9 |
| CD4 Thymus CA 1 | 36 | 25.798 | 3.2 | 22.8 | 10.1 |
| CD4 Thymus CA 3 | 38 | 26.069 | 3.3 | 21.9 | 12.4 |
| CD4 Thymus CAKR 1 | 30 | 22.058 | 3.3 | 20.5 | 6.5 |
| CD4 Thymus CAKR 2 | 32 | 38.268 | 6.9 | 18.5 | 6.8 |
| CD4 Thymus CAKR 3 | 38 | 23.262 | 4.2 | 24.9 | 8.4 |
| CD4 Thymus WT 2 | 23 | 8.824 | 3.4 | 13.6 | 5.8 |
| CD4 Thymus WT 3 | 19 | 6.685 | 3.8 | 10.7 | 4.5 |
| CD8 Lymph nodes CA 2 | 21 | 0.076 | 2.8 | 8.4 | 9.9 |
| CD8 Lymph nodes CA 3 | 20 | 0.137 | 2.6 | 8.5 | 9.1 |
| CD8 Lymph nodes CAKR 2 | 24 | 0.084 | 3.0 | 12.2 | 8.7 |
| CD8 Lymph nodes CAKR 3 | 20 | 0.080 | 2.7 | 8.9 | 8.2 |
| CD8 Lymph nodes WT 1 | 23 | 0.117 | 2.8 | 10.8 | 9.6 |
| CD8 Lymph nodes WT 2 | 21 | 0.051 | 2.4 | 9.1 | 9.8 |
| CD8 Lymph nodes WT 3 | 21 | 0.091 | 2.4 | 8.9 | 9.5 |
| CD8 Thymus CA 1 | 22 | 0.219 | 2.9 | 11.6 | 7.8 |
| CD8 Thymus CA 3 | 19 | 0.102 | 2.4 | 8.4 | 7.8 |
| CD8 Thymus CAKR 2 | 23 | 0.521 | 2.8 | 12.4 | 7.6 |
| CD8 Thymus CAKR 3 | 19 | 0.188 | 3.2 | 10.5 | 5.6 |
| CD8 Thymus WT 1 | 23 | 0.126 | 2.2 | 11.1 | 10.1 |
| CD8 Thymus WT 2 | 26 | 0.115 | 2.7 | 12.9 | 10.2 |
| CD8 Thymus WT 3 | 20 | 0.126 | 2.3 | 8.6 | 9.0 |
# source data
write.csv(nkt_table, "source_data/FigED7b_nkt_cells.csv", row.names = F)## Error in file(file, ifelse(append, "a", "w")): cannot open the connection
Now we will plot the data for CD4 cells and for CD8 cells. Plots for TRA and TRB sequences are separated.
This plot visualizes the percentage of any of the TRB NKT chains (TRBV1, TRBV13-2 and TRBV29). We can see that this distinction is not specific as these gene segments are also used by conventional CD4 and CD8 T cells.
## CD4
p01 <- nkt_table_trb %>%
filter(Cell_type == "CD4") %>%
ggplot(aes(y = freq*100, x = factor(sample_type, levels = levels_cd4))) +
stat_summary(fun = "mean",
geom = "crossbar",
width = 0.5,
colour = "black") +
geom_point(aes(color = Mouse_strain)) +
ylab("Percentage of all chains") +
ggtitle("NKT segments in CD4 TRB") +
theme_classic() +
ggtheme() +
scale_color_manual(values = c("dodgerblue","indianred2","gray40")) +
ylim(c(0,NA)) + xlab("")
#ggsave("./final_fig/nkt/pct_all_nkt_trb_cd4.png", width = 8, height = 8, units = "cm")
#ggsave("./final_fig/nkt/pct_all_nkt_trb_cd4.eps", width = 8, height = 8, units = "cm")
## CD8
p02 <- nkt_table_trb %>%
filter(Cell_type == "CD8") %>%
ggplot(aes(y = freq*100, x = factor(sample_type, levels = levels_cd8))) +
stat_summary(fun = "mean",
geom = "crossbar",
width = 0.5,
colour = "black") +
geom_point(aes(color = Mouse_strain)) +
ylab("Percentage of all chains") +
ggtitle("NKT segments in CD8 TRB") +
theme_classic() +
ggtheme() +
scale_color_manual(values = c("dodgerblue","indianred2","gray40")) +
ylim(c(0,NA)) + xlab("")
#ggsave("./final_fig/nkt/pct_all_nkt_trb_cd8.png", width = 8, height = 8, units = "cm")
#ggsave("./final_fig/nkt/pct_all_nkt_trb_cd8.eps", width = 8, height = 8, units = "cm")
p01 + p02## CD4
p03 <- nkt_table_trav11_traj18 %>%
filter(Cell_type == "CD4") %>%
ggplot(aes(y = freq*100, x = factor(sample_type, levels = levels_cd4))) +
stat_summary(fun = "mean",
geom = "crossbar",
width = 0.5,
colour = "black") +
geom_point(aes(color = Mouse_strain)) +
ylab("Percentage of all chains") +
ggtitle("TRAV11-TRAJ18 segments in CD4 cells") +
theme_classic() +
ggtheme() +
scale_color_manual(values = c("dodgerblue","indianred2","gray40")) +
ylim(c(0,NA)) + xlab("")
#ggsave("./final_fig/nkt/pct_nkt_trav11_traj18_tra_cd4.png", width = 8, height = 8, units = "cm")
#ggsave("./final_fig/nkt/pct_nkt_trav11_traj18_tra_cd4.eps", width = 8, height = 8, units = "cm")
## CD8
p04 <- nkt_table_trav11_traj18 %>%
filter(Cell_type == "CD8") %>%
ggplot(aes(y = freq*100, x = factor(sample_type, levels = levels_cd8))) +
stat_summary(fun = "mean",
geom = "crossbar",
width = 0.5,
colour = "black") +
geom_point(aes(color = Mouse_strain)) +
ylab("Percentage of all chains") +
ggtitle("TRAV11-TRAJ18 segments in CD8 cells") +
theme_classic() +
ggtheme() +
scale_color_manual(values = c("dodgerblue","indianred2","gray40")) +
ylim(c(0,1)) + xlab("")
#ggsave("./final_fig/nkt/pct_nkt_trav11_traj18_tra_cd8.png", width = 8, height = 8, units = "cm")
#ggsave("./final_fig/nkt/pct_nkt_trav11_traj18_tra_cd8.eps", width = 8, height = 8, units = "cm")
p03 + p04## CD4
p05 <- nkt_table_trbv1 %>%
filter(Cell_type == "CD4") %>%
ggplot(aes(y = freq*100, x = factor(sample_type, levels = levels_cd4))) +
stat_summary(fun = "mean",
geom = "crossbar",
width = 0.5,
colour = "black") +
geom_point(aes(color = Mouse_strain)) +
ylab("Percentage of all chains") +
ggtitle("CD4 TRBV1") +
theme_classic() +
ggtheme() +
scale_color_manual(values = c("dodgerblue","indianred2","gray40")) +
ylim(c(0,NA)) + xlab("")
#ggsave("./final_fig/nkt/pct_nkt_trbv1_cd4.png", width = 8, height = 8, units = "cm")
#ggsave("./final_fig/nkt/pct_nkt_trbv1_cd4.eps", width = 8, height = 8, units = "cm")
## CD8
p06 <- nkt_table_trbv1 %>%
filter(Cell_type == "CD8") %>%
ggplot(aes(y = freq*100, x = factor(sample_type, levels = levels_cd8))) +
stat_summary(fun = "mean",
geom = "crossbar",
width = 0.5,
colour = "black") +
geom_point(aes(color = Mouse_strain)) +
ylab("Percentage of all chains") +
ggtitle("CD8 TRBV1") +
theme_classic() +
ggtheme() +
scale_color_manual(values = c("dodgerblue","indianred2","gray40")) +
ylim(c(0,NA)) + xlab("")
#ggsave("./final_fig/nkt/pct_nkt_trbv1_cd8.png", width = 8, height = 8, units = "cm")
#ggsave("./final_fig/nkt/pct_nkt_trbv1_cd8.eps", width = 8, height = 8, units = "cm")
p05 + p06## CD4
p07 <- nkt_table_trbv13_2 %>%
filter(Cell_type == "CD4") %>%
ggplot(aes(y = freq*100, x = factor(sample_type, levels = levels_cd4))) +
stat_summary(fun = "mean",
geom = "crossbar",
width = 0.5,
colour = "black") +
geom_point(aes(color = Mouse_strain)) +
ylab("Percentage of all chains") +
ggtitle("TRBV13-2 segments in CD4 cells") +
theme_classic() +
ggtheme() +
scale_color_manual(values = c("dodgerblue","indianred2","gray40")) +
ylim(c(0,NA)) + xlab("")
#ggsave("./final_fig/nkt/pct_nkt_trbv13_2_cd4.png", width = 8, height = 8, units = "cm")
#ggsave("./final_fig/nkt/pct_nkt_trbv13_2_cd4.eps", width = 8, height = 8, units = "cm")
## CD8
p08 <- nkt_table_trbv13_2 %>%
filter(Cell_type == "CD8") %>%
ggplot(aes(y = freq*100, x = factor(sample_type, levels = levels_cd8))) +
stat_summary(fun = "mean",
geom = "crossbar",
width = 0.5,
colour = "black") +
geom_point(aes(color = Mouse_strain)) +
ylab("Percentage of all chains") +
ggtitle("TRBV13-2 segments in CD8 cells") +
theme_classic() +
ggtheme() +
scale_color_manual(values = c("dodgerblue","indianred2","gray40")) +
ylim(c(0,NA)) + xlab("")
#ggsave("./final_fig/nkt/pct_nkt_trbv13_2_cd8.png", width = 8, height = 8, units = "cm")
#ggsave("./final_fig/nkt/pct_nkt_trbv13_2_cd8.eps", width = 8, height = 8, units = "cm")
p07 + p08## CD4
p09 <- nkt_table_trbv29 %>%
filter(Cell_type == "CD4") %>%
ggplot(aes(y = freq*100, x = factor(sample_type, levels = levels_cd4))) +
stat_summary(fun = "mean",
geom = "crossbar",
width = 0.5,
colour = "black") +
geom_point(aes(color = Mouse_strain)) +
ylab("Percentage of all chains") +
ggtitle("CD4 TRBV29") +
theme_classic() +
ggtheme() +
scale_color_manual(values = c("dodgerblue","indianred2","gray40")) +
ylim(c(0,NA)) + xlab("")
#ggsave("./final_fig/nkt/pct_nkt_trbv29_cd4.png", width = 8, height = 8, units = "cm")
#ggsave("./final_fig/nkt/pct_nkt_trbv29_cd4.eps", width = 8, height = 8, units = "cm")
## CD8
p10 <- nkt_table_trbv29 %>%
filter(Cell_type == "CD8") %>%
ggplot(aes(y = freq*100, x = factor(sample_type, levels = levels_cd8))) +
stat_summary(fun = "mean",
geom = "crossbar",
width = 0.5,
colour = "black") +
geom_point(aes(color = Mouse_strain)) +
ylab("Percentage of all chains") +
ggtitle("CD8 TRBV29") +
theme_classic() +
ggtheme() +
scale_color_manual(values = c("dodgerblue","indianred2","gray40")) +
ylim(c(0,NA)) + xlab("")
#ggsave("./final_fig/nkt/pct_nkt_trbv29_cd8.png", width = 8, height = 8, units = "cm")
#ggsave("./final_fig/nkt/pct_nkt_trbv29_cd8.eps", width = 8, height = 8, units = "cm")
p09 + p10In this part we explore the similarity of TCR repertoires of different samples using principal component analysis (PCA).
We will calculate the distances from a filtered matrix of proportions, in which we filtered out the CDR3 sequences that are present in less than five CD4 or in less than five CD8 samples.
First, we will prepare the filtered normalized count matrix.
Below, we find and save the CDR3 TRA/TRB amino acid sequences, which are present in 5 or more samples out of 14 CD4 samples, or in 5 or more samples out of 14 CD8 samples.
# TRA
keep_tra_cd4 <- excel_count_table_tra %>%
mutate(nkt_trav11_traj18 = if_else(
(grepl(allVHitsWithScore, pattern = "TRAV11") |
(grepl(allJHitsWithScore, pattern = "TRAJ18"))),"yes","no")) %>%
filter(nkt_trav11_traj18 == "no") %>%
select(aaSeqCDR3, starts_with("CD4")) %>%
mutate_at(vars(starts_with("CD4")), .funs = binary)
keep_tra_cd4$sum <- rowSums((keep_tra_cd4 %>% select(-aaSeqCDR3)))
keep_tra_cd4$keep <- ifelse(keep_tra_cd4$sum>4,1,0)
keep_tra_cd4$orig <- ifelse(keep_tra_cd4$sum>0,1,0)
keep_tra_cd4_sequences <- pull(keep_tra_cd4 %>% filter(keep == 1), aaSeqCDR3)
# TRB
keep_trb_cd4 <- excel_count_table_trb %>% select(aaSeqCDR3, starts_with("CD4")) %>% mutate_at(vars(starts_with("CD4")), .funs = binary)
keep_trb_cd4$sum <- rowSums((keep_trb_cd4 %>% select(-aaSeqCDR3)))
keep_trb_cd4$keep <- ifelse(keep_trb_cd4$sum>4,1,0)
keep_trb_cd4$orig <- ifelse(keep_trb_cd4$sum>0,1,0)
keep_trb_cd4_sequences <- pull(keep_trb_cd4 %>% filter(keep == 1), aaSeqCDR3)
# TRA
keep_tra_cd8 <- excel_count_table_tra %>%
mutate(nkt_trav11_traj18 = if_else(
(grepl(allVHitsWithScore, pattern = "TRAV11") |
(grepl(allJHitsWithScore, pattern = "TRAJ18"))),"yes","no")) %>%
filter(nkt_trav11_traj18 == "no") %>%
select(aaSeqCDR3, starts_with("CD8")) %>%
mutate_at(vars(starts_with("CD8")), .funs = binary)
keep_tra_cd8$sum <- rowSums((keep_tra_cd8 %>% select(-aaSeqCDR3)))
keep_tra_cd8$keep <- ifelse(keep_tra_cd8$sum>4,1,0)
keep_tra_cd8$orig <- ifelse(keep_tra_cd8$sum>0,1,0)
keep_tra_cd8_sequences <- pull(keep_tra_cd8 %>% filter(keep == 1), aaSeqCDR3)
# TRB
keep_trb_cd8 <- excel_count_table_trb %>% select(aaSeqCDR3, starts_with("CD8")) %>% mutate_at(vars(starts_with("CD8")), .funs = binary)
keep_trb_cd8$sum <- rowSums((keep_trb_cd8 %>% select(-aaSeqCDR3)))
keep_trb_cd8$keep <- ifelse(keep_trb_cd8$sum>4,1,0)
keep_trb_cd8$orig <- ifelse(keep_trb_cd8$sum>0,1,0)
keep_trb_cd8_sequences <- pull(keep_trb_cd8 %>% filter(keep == 1), aaSeqCDR3)
cdr3_count_table <- data.frame(Sample = c("CD4 TRA","CD4 TRB","CD8 TRA","CD8 TRB"),
`Count before filtering` = c(
length(pull(keep_tra_cd4 %>% filter(orig == 1), aaSeqCDR3)),
length(pull(keep_trb_cd4 %>% filter(orig == 1), aaSeqCDR3)),
length(pull(keep_tra_cd8 %>% filter(orig == 1), aaSeqCDR3)),
length(pull(keep_trb_cd8 %>% filter(orig == 1), aaSeqCDR3))),
`Count after filtering` = c(
length(pull(keep_tra_cd4 %>% filter(keep == 1), aaSeqCDR3)),
length(pull(keep_trb_cd4 %>% filter(keep == 1), aaSeqCDR3)),
length(pull(keep_tra_cd8 %>% filter(keep == 1), aaSeqCDR3)),
length(pull(keep_trb_cd8 %>% filter(keep == 1), aaSeqCDR3))))Here you can check the counts of CDR3 sequences before and after filtering.
kable(cdr3_count_table) %>%
kable_styling(full_width = F, font_size = 11,
bootstrap_options = c("striped", "hover", "condensed", "responsive"))| Sample | Count.before.filtering | Count.after.filtering |
|---|---|---|
| CD4 TRA | 64812 | 3406 |
| CD4 TRB | 245611 | 7201 |
| CD8 TRA | 65540 | 3227 |
| CD8 TRB | 238965 | 7808 |
Now we will create filtered tables with the abundant sequences.
# TRB
excel_count_table_trb3_filter <- excel_count_table_trb2 %>% filter(aaSeqCDR3 %in% keep_trb_cd4_sequences | aaSeqCDR3 %in% keep_trb_cd8_sequences)
count_table_trb4_filter <- as.matrix(excel_count_table_trb3_filter[,5:32])
rownames(count_table_trb4_filter) <- excel_count_table_trb3_filter$aaSeqCDR3
trb4_norm_filter <- scale(count_table_trb4_filter, center=FALSE, scale=colSums(count_table_trb4_filter))
prop.table.trb_filter <- cbind(trb4_norm_filter, excel_count_table_trb3_filter %>% select(-starts_with("CD")) )
# TRA
excel_count_table_tra3_filter <- excel_count_table_tra3 %>% filter(aaSeqCDR3 %in% keep_tra_cd4_sequences | aaSeqCDR3 %in% keep_tra_cd8_sequences) %>% filter(nkt_trav11_traj18 == "no")
count_table_tra4_filter <- as.matrix(excel_count_table_tra3_filter[,5:32])
rownames(count_table_tra4_filter) <- excel_count_table_tra3_filter$aaSeqCDR3
tra4_norm_filter <- scale(count_table_tra4_filter, center=FALSE, scale=colSums(count_table_tra4_filter))
prop.table.tra_filter <- cbind(tra4_norm_filter, excel_count_table_tra3_filter %>% select(-starts_with("CD")) )
colnames(prop.table.tra_filter)## [1] "CD4 Lymph nodes CA Exp01" "CD4 Lymph nodes CA Exp02"
## [3] "CD4 Lymph nodes CA Exp03" "CD4 Lymph nodes CAKR Exp02"
## [5] "CD4 Lymph nodes CAKR Exp03" "CD4 Lymph nodes WT Exp02"
## [7] "CD4 Lymph nodes WT Exp03" "CD4 Thymus CA Exp01"
## [9] "CD4 Thymus CA Exp03" "CD4 Thymus CAKR Exp01"
## [11] "CD4 Thymus CAKR Exp02" "CD4 Thymus CAKR Exp03"
## [13] "CD4 Thymus WT Exp02" "CD4 Thymus WT Exp03"
## [15] "CD8 Lymph nodes CA Exp02" "CD8 Lymph nodes CA Exp03"
## [17] "CD8 Lymph nodes CAKR Exp02" "CD8 Lymph nodes CAKR Exp03"
## [19] "CD8 Lymph nodes WT Exp01" "CD8 Lymph nodes WT Exp02"
## [21] "CD8 Lymph nodes WT Exp03" "CD8 Thymus CA Exp01"
## [23] "CD8 Thymus CA Exp03" "CD8 Thymus CAKR Exp02"
## [25] "CD8 Thymus CAKR Exp03" "CD8 Thymus WT Exp01"
## [27] "CD8 Thymus WT Exp02" "CD8 Thymus WT Exp03"
## [29] "aaSeqCDR3" "allDHitsWithScore"
## [31] "allJHitsWithScore" "allVHitsWithScore"
## [33] "nkt_trav11_traj18"
Last, we will select the columns of the filtered table corresponding to CD4 and CD8 cells. We will remove the rows in which there are only zeros for the subset of CD4 or CD8 cells, respectively.
prop.table.tra2_filter.cd4 <- prop.table.tra_filter[,1:14]
prop.table.tra2_filter.cd4$sum <- rowSums(prop.table.tra2_filter.cd4)
prop.table.tra2_filter.cd4 <- prop.table.tra2_filter.cd4 %>% filter(sum >0) %>% select(-sum)
prop.table.trb2_filter.cd4 <- prop.table.trb_filter[,1:14]
prop.table.trb2_filter.cd4$sum <- rowSums(prop.table.trb2_filter.cd4)
prop.table.trb2_filter.cd4 <- prop.table.trb2_filter.cd4 %>% filter(sum >0) %>% select(-sum)
prop.table.tra2_filter.cd8 <- prop.table.tra_filter[,15:28]
prop.table.tra2_filter.cd8$sum <- rowSums(prop.table.tra2_filter.cd8)
prop.table.tra2_filter.cd8 <- prop.table.tra2_filter.cd8 %>% filter(sum >0) %>% select(-sum)
prop.table.trb2_filter.cd8 <- prop.table.trb_filter[,15:28]
prop.table.trb2_filter.cd8$sum <- rowSums(prop.table.trb2_filter.cd8)
prop.table.trb2_filter.cd8 <- prop.table.trb2_filter.cd8 %>% filter(sum >0) %>% select(-sum)Using the tables that we generated previously, we will compute the
PCA with the package factoextra. First, we will merge TRA
and TRB CDR3 sequences together and the we will also perform separated
analyses.
prop.table.tra2_filter.merge <- rbind(prop.table.tra2_filter.cd4, prop.table.trb2_filter.cd4)
res.pca.merge.cd4 <- prcomp(t(prop.table.tra2_filter.merge), scale = TRUE, center = T)
mdres.pca.merge.cd4 <- colnames(prop.table.tra2_filter.merge[,1:14]) %>%
as.data.frame() %>%
mutate(sample = stringr::str_replace_all(., pattern = "Lymph nodes", replacement = "LN")) %>%
separate(sample, into = c("Cell_type","Organ","Strain","Exp"))
fviz_pca_ind(res.pca.merge.cd4,
col.ind = as.factor(mdres.pca.merge.cd4$Strain), # color by groups
legend.title = "Groups",
geom.ind = "point",
invisible="quali", pointsize = 0) +
scale_color_manual(values=c("dodgerblue","indianred2","gray40")) +
geom_point(aes(shape = as.factor(mdres.pca.merge.cd4$Organ),
color = as.factor(mdres.pca.merge.cd4$Strain))) +
scale_shape_manual(values = c(8,8,8,15,15)) +
ggtheme() +
theme(axis.text.x = element_text(angle = 0))# source data
# write.csv(res.pca.merge.cd4$x, "source_data/pca_raw_cd4.csv")
#ggsave("final_fig/pca/cd4.png", width = 2.6, height = 1.7)
#ggsave("final_fig/pca/cd4.svg", width = 2.6, height = 1.7)As the points in the central location are overlapping, we will also create a zoom plot. This will be added to the previous figure manually in a graphic editor.
fviz_pca_ind(res.pca.merge.cd4,
col.ind = as.factor(mdres.pca.merge.cd4$Strain), # color by groups
legend.title = "Groups",
geom.ind = "point",
invisible="quali", pointsize = 0) +
scale_color_manual(values=c("dodgerblue","indianred2","gray40")) +
geom_point(aes(shape = as.factor(mdres.pca.merge.cd4$Organ),
color = as.factor(mdres.pca.merge.cd4$Strain))) +
scale_shape_manual(values = c(8,8,8,15,15)) +
xlim(c(-30,0)) + ylim(-2,15) +
ggtheme() +
theme(axis.text.x = element_text(angle = 0))#ggsave("final_fig/pca/cd4_zoom.png", width = 2.2, height = 1.4)
#ggsave("final_fig/pca/cd4_zoom.svg", width = 2.2, height = 1.4)prop.table.tra2_filter.merge <- rbind(prop.table.tra2_filter.cd8, prop.table.trb2_filter.cd8)
res.pca.merge.cd8 <- prcomp(t(prop.table.tra2_filter.merge), scale = TRUE, center = T)
mdres.pca.merge.cd8 <- colnames(prop.table.tra2_filter.merge[,1:14]) %>%
as.data.frame() %>%
mutate(sample = stringr::str_replace_all(., pattern = "Lymph nodes", replacement = "LN")) %>%
separate(sample, into = c("Cell_type","Organ","Strain","Exp"))
fviz_pca_ind(res.pca.merge.cd8,
col.ind = as.factor(mdres.pca.merge.cd8$Strain), # color by groups
legend.title = "Groups",
geom.ind = "point",
invisible="quali", pointsize = 0) +
scale_color_manual(values=c("dodgerblue","indianred2","gray40")) +
geom_point(aes(shape = as.factor(mdres.pca.merge.cd8$Organ),
color = as.factor(mdres.pca.merge.cd8$Strain))) +
scale_shape_manual(values = c(8,8,8,15,15)) +
ggtheme()# source data
write.csv(res.pca.merge.cd8$x, "source_data/pca_raw_cd8.csv")
#ggsave("final_fig/pca/cd8.png", width = 2.6, height = 1.7)
#ggsave("final_fig/pca/cd8.svg", width = 2.6, height = 1.7)As the points in the central location are overlapping, we will also create a zoom plot. This will be added to the previous figure manually in a graphic editor.
fviz_pca_ind(res.pca.merge.cd8,
col.ind = as.factor(mdres.pca.merge.cd8$Strain), # color by groups
legend.title = "Groups",
geom.ind = "point",
invisible="quali", pointsize = 0) +
scale_color_manual(values=c("dodgerblue","indianred2","gray40")) +
geom_point(aes(shape = as.factor(mdres.pca.merge.cd8$Organ),
color = as.factor(mdres.pca.merge.cd8$Strain))) +
scale_shape_manual(values = c(8,8,8,15,15)) +
xlim(c(-32,-18)) + ylim(-4,5) +
ggtheme()#ggsave("final_fig/pca/cd8_zoom.png", width = 2.2, height = 1.4)
#ggsave("final_fig/pca/cd8_zoom.svg", width = 2.2, height = 1.4)Next, we will create eight separate plots which show the PCA for TRA/TRB CDR3 sequences from each cell type and each organ.
## Thymus
prop.table.tra2_filter.cd4.thy <- prop.table.tra_filter[,8:14]
prop.table.tra2_filter.cd4.thy$sum <- rowSums(prop.table.tra2_filter.cd4.thy)
prop.table.tra2_filter.cd4.thy <- prop.table.tra2_filter.cd4.thy %>% filter(sum >0) %>% select(-sum)
res.pca.tra.cd4.thy <- prcomp(t(prop.table.tra2_filter.cd4.thy), scale = TRUE, center = T)
mdres.pca.tra.cd4.thy <- colnames(prop.table.tra_filter[,8:14]) %>%
as.data.frame() %>%
mutate(sample = stringr::str_replace_all(., pattern = "Lymph nodes", replacement = "LN")) %>%
separate(sample, into = c("Cell_type","Organ","Strain","Exp"))
fviz_pca_ind(res.pca.tra.cd4.thy,
col.ind = as.factor(mdres.pca.tra.cd4.thy$Strain), # color by groups
legend.title = "Groups",
geom.ind = "point",
invisible="quali", pointsize = 0) +
scale_color_manual(values=c("dodgerblue","indianred2","gray40")) +
geom_point(aes(shape = as.factor(mdres.pca.tra.cd4.thy$Organ),
color = as.factor(mdres.pca.tra.cd4.thy$Strain))) +
geom_hline(yintercept = 0, color = "grey20") +
geom_vline(xintercept = 0, color = "grey20") +
ggtheme()#ggsave("final_fig/pca/cd4_tra_thy.png", width = 2.6, height = 1.7)
#ggsave("final_fig/pca/cd4_tra_thy.svg", width = 2.6, height = 1.7)## LN
prop.table.tra2_filter.cd4.ln <- prop.table.tra_filter[,1:7]
prop.table.tra2_filter.cd4.ln$sum <- rowSums(prop.table.tra2_filter.cd4.ln)
prop.table.tra2_filter.cd4.ln <- prop.table.tra2_filter.cd4.ln %>% filter(sum >0) %>% select(-sum)
res.pca.tra.cd4.ln <- prcomp(t(prop.table.tra2_filter.cd4.ln), scale = TRUE, center = T)
mdres.pca.tra.cd4.ln <- colnames(prop.table.tra_filter[,1:7]) %>%
as.data.frame() %>%
mutate(sample = stringr::str_replace_all(., pattern = "Lymph nodes", replacement = "LN")) %>%
separate(sample, into = c("Cell_type","Organ","Strain","Exp"))
fviz_pca_ind(res.pca.tra.cd4.ln,
col.ind = as.factor(mdres.pca.tra.cd4.ln$Strain), # color by groups
legend.title = "Groups",
geom.ind = "point",
invisible="quali", pointsize = 0) +
scale_color_manual(values=c("dodgerblue","indianred2","gray40")) +
geom_point(aes(shape = as.factor(mdres.pca.tra.cd4.ln$Organ),
color = as.factor(mdres.pca.tra.cd4.ln$Strain))) +
geom_hline(yintercept = 0, color = "grey20") +
geom_vline(xintercept = 0, color = "grey20") #ggsave("final_fig/pca/cd4_tra_ln.png", width = 2.6, height = 1.7)
#ggsave("final_fig/pca/cd4_tra_ln.svg", width = 2.6, height = 1.7)## Thymus
prop.table.tra2_filter %>% colnames## Error in is.data.frame(x): object 'prop.table.tra2_filter' not found
prop.table.tra2_filter.cd8.thy <- prop.table.tra_filter[,22:28]
prop.table.tra2_filter.cd8.thy$sum <- rowSums(prop.table.tra2_filter.cd8.thy)
prop.table.tra2_filter.cd8.thy <- prop.table.tra2_filter.cd8.thy %>% filter(sum >0) %>% select(-sum)
res.pca.tra.cd8.thy <- prcomp(t(prop.table.tra2_filter.cd8.thy), scale = TRUE, center = T)
mdres.pca.tra.cd8.thy <- colnames(prop.table.tra_filter[,22:28]) %>%
as.data.frame() %>%
mutate(sample = stringr::str_replace_all(., pattern = "Lymph nodes", replacement = "LN")) %>%
separate(sample, into = c("Cell_type","Organ","Strain","Exp"))
fviz_pca_ind(res.pca.tra.cd8.thy,
col.ind = as.factor(mdres.pca.tra.cd8.thy$Strain), # color by groups
legend.title = "Groups",
geom.ind = "point",
invisible="quali", pointsize = 0) +
scale_color_manual(values=c("dodgerblue","indianred2","gray40")) +
geom_point(aes(shape = as.factor(mdres.pca.tra.cd8.thy$Organ),
color = as.factor(mdres.pca.tra.cd8.thy$Strain))) +
geom_hline(yintercept = 0, color = "grey20") +
geom_vline(xintercept = 0, color = "grey20") #ggsave("final_fig/pca/cd8_tra_thy.png", width = 2.6, height = 1.7)
#ggsave("final_fig/pca/cd8_tra_thy.svg", width = 2.6, height = 1.7)## LN
prop.table.tra2_filter %>% colnames## Error in is.data.frame(x): object 'prop.table.tra2_filter' not found
prop.table.tra2_filter.cd8.ln <- prop.table.tra_filter[,15:21]
prop.table.tra2_filter.cd8.ln$sum <- rowSums(prop.table.tra2_filter.cd8.ln)
prop.table.tra2_filter.cd8.ln <- prop.table.tra2_filter.cd8.ln %>% filter(sum >0) %>% select(-sum)
res.pca.tra.cd8.ln <- prcomp(t(prop.table.tra2_filter.cd8.ln), scale = TRUE, center = T)
mdres.pca.tra.cd8.ln <- colnames(prop.table.tra_filter[,15:21]) %>%
as.data.frame() %>%
mutate(sample = stringr::str_replace_all(., pattern = "Lymph nodes", replacement = "LN")) %>%
separate(sample, into = c("Cell_type","Organ","Strain","Exp"))
fviz_pca_ind(res.pca.tra.cd8.ln,
col.ind = as.factor(mdres.pca.tra.cd8.ln$Strain), # color by groups
legend.title = "Groups",
geom.ind = "point",
invisible="quali", pointsize = 0) +
scale_color_manual(values=c("dodgerblue","indianred2","gray40")) +
geom_point(aes(shape = as.factor(mdres.pca.tra.cd8.ln$Organ),
color = as.factor(mdres.pca.tra.cd8.ln$Strain))) +
geom_hline(yintercept = 0, color = "grey20") +
geom_vline(xintercept = 0, color = "grey20") #ggsave("final_fig/pca/cd8_tra_ln.png", width = 2.6, height = 1.7)
#ggsave("final_fig/pca/cd8_tra_ln.svg", width = 2.6, height = 1.7)## Thymus
prop.table.trb_filter %>% colnames## [1] "CD4 Lymph nodes CA Exp01" "CD4 Lymph nodes CA Exp02"
## [3] "CD4 Lymph nodes CA Exp03" "CD4 Lymph nodes CAKR Exp02"
## [5] "CD4 Lymph nodes CAKR Exp03" "CD4 Lymph nodes WT Exp02"
## [7] "CD4 Lymph nodes WT Exp03" "CD4 Thymus CA Exp01"
## [9] "CD4 Thymus CA Exp03" "CD4 Thymus CAKR Exp01"
## [11] "CD4 Thymus CAKR Exp02" "CD4 Thymus CAKR Exp03"
## [13] "CD4 Thymus WT Exp02" "CD4 Thymus WT Exp03"
## [15] "CD8 Lymph nodes CA Exp02" "CD8 Lymph nodes CA Exp03"
## [17] "CD8 Lymph nodes CAKR Exp02" "CD8 Lymph nodes CAKR Exp03"
## [19] "CD8 Lymph nodes WT Exp01" "CD8 Lymph nodes WT Exp02"
## [21] "CD8 Lymph nodes WT Exp03" "CD8 Thymus CA Exp01"
## [23] "CD8 Thymus CA Exp03" "CD8 Thymus CAKR Exp02"
## [25] "CD8 Thymus CAKR Exp03" "CD8 Thymus WT Exp01"
## [27] "CD8 Thymus WT Exp02" "CD8 Thymus WT Exp03"
## [29] "aaSeqCDR3" "allVHitsWithScore"
## [31] "allDHitsWithScore" "allJHitsWithScore"
prop.table.trb2_filter.cd4.thy <- prop.table.trb_filter[,12:18]
prop.table.trb2_filter.cd4.thy$sum <- rowSums(prop.table.trb2_filter.cd4.thy)
prop.table.trb2_filter.cd4.thy <- prop.table.trb2_filter.cd4.thy %>% filter(sum >0) %>% select(-sum)
res.pca.trb.cd4.thy <- prcomp(t(prop.table.trb2_filter.cd4.thy), scale = TRUE, center = T)
mdres.pca.trb.cd4.thy <- colnames(prop.table.trb_filter[,12:18]) %>%
as.data.frame() %>%
mutate(sample = stringr::str_replace_all(., pattern = "Lymph nodes", replacement = "LN")) %>%
separate(sample, into = c("Cell_type","Organ","Strain","Exp"))
fviz_pca_ind(res.pca.trb.cd4.thy,
col.ind = as.factor(mdres.pca.trb.cd4.thy$Strain), # color by groups
legend.title = "Groups",
geom.ind = "point",
invisible="quali", pointsize = 0) +
scale_color_manual(values=c("dodgerblue","indianred2","gray40")) +
geom_point(aes(shape = as.factor(mdres.pca.trb.cd4.thy$Organ),
color = as.factor(mdres.pca.trb.cd4.thy$Strain))) +
geom_hline(yintercept = 0, color = "grey20") +
geom_vline(xintercept = 0, color = "grey20") #ggsave("final_fig/pca/cd4_trb_thy.png", width = 2.6, height = 1.7)
#ggsave("final_fig/pca/cd4_trb_thy.svg", width = 2.6, height = 1.7)## All
colnames(prop.table.trb2_filter)## Error in is.data.frame(x): object 'prop.table.trb2_filter' not found
prop.table.trb2_filter.cd4 <- prop.table.trb_filter[,5:18]
prop.table.trb2_filter.cd4$sum <- rowSums(prop.table.trb2_filter.cd4)
prop.table.trb2_filter.cd4 <- prop.table.trb2_filter.cd4 %>% filter(sum >0) %>% select(-sum)
res.pca.trb.cd4 <- prcomp(t(prop.table.trb2_filter.cd4), scale = TRUE, center = T)
mdres.pca.trb.cd4 <- colnames(prop.table.trb_filter[,5:18]) %>%
as.data.frame() %>%
mutate(sample = stringr::str_replace_all(., pattern = "Lymph nodes", replacement = "LN")) %>%
separate(sample, into = c("Cell_type","Organ","Strain","Exp"))
## LN
prop.table.trb_filter %>% colnames## [1] "CD4 Lymph nodes CA Exp01" "CD4 Lymph nodes CA Exp02"
## [3] "CD4 Lymph nodes CA Exp03" "CD4 Lymph nodes CAKR Exp02"
## [5] "CD4 Lymph nodes CAKR Exp03" "CD4 Lymph nodes WT Exp02"
## [7] "CD4 Lymph nodes WT Exp03" "CD4 Thymus CA Exp01"
## [9] "CD4 Thymus CA Exp03" "CD4 Thymus CAKR Exp01"
## [11] "CD4 Thymus CAKR Exp02" "CD4 Thymus CAKR Exp03"
## [13] "CD4 Thymus WT Exp02" "CD4 Thymus WT Exp03"
## [15] "CD8 Lymph nodes CA Exp02" "CD8 Lymph nodes CA Exp03"
## [17] "CD8 Lymph nodes CAKR Exp02" "CD8 Lymph nodes CAKR Exp03"
## [19] "CD8 Lymph nodes WT Exp01" "CD8 Lymph nodes WT Exp02"
## [21] "CD8 Lymph nodes WT Exp03" "CD8 Thymus CA Exp01"
## [23] "CD8 Thymus CA Exp03" "CD8 Thymus CAKR Exp02"
## [25] "CD8 Thymus CAKR Exp03" "CD8 Thymus WT Exp01"
## [27] "CD8 Thymus WT Exp02" "CD8 Thymus WT Exp03"
## [29] "aaSeqCDR3" "allVHitsWithScore"
## [31] "allDHitsWithScore" "allJHitsWithScore"
prop.table.trb2_filter.cd4.ln <- prop.table.trb_filter[,5:11]
prop.table.trb2_filter.cd4.ln$sum <- rowSums(prop.table.trb2_filter.cd4.ln)
prop.table.trb2_filter.cd4.ln <- prop.table.trb2_filter.cd4.ln %>% filter(sum >0) %>% select(-sum)
res.pca.trb.cd4.ln <- prcomp(t(prop.table.trb2_filter.cd4.ln), scale = TRUE, center = T)
mdres.pca.trb.cd4.ln <- colnames(prop.table.trb_filter[,5:11]) %>%
as.data.frame() %>%
mutate(sample = stringr::str_replace_all(., pattern = "Lymph nodes", replacement = "LN")) %>%
separate(sample, into = c("Cell_type","Organ","Strain","Exp"))
fviz_pca_ind(res.pca.trb.cd4.ln,
col.ind = as.factor(mdres.pca.trb.cd4.ln$Strain), # color by groups
legend.title = "Groups",
geom.ind = "point",
invisible="quali", pointsize = 0) +
scale_color_manual(values=c("dodgerblue","indianred2","gray40")) +
geom_point(aes(shape = as.factor(mdres.pca.trb.cd4.ln$Organ),
color = as.factor(mdres.pca.trb.cd4.ln$Strain))) +
geom_hline(yintercept = 0, color = "grey20") +
geom_vline(xintercept = 0, color = "grey20") #ggsave("final_fig/pca/cd4_trb_ln.png", width = 2.6, height = 1.7)
#ggsave("final_fig/pca/cd4_trb_ln.svg", width = 2.6, height = 1.7)## Thymus
prop.table.trb_filter %>% colnames## [1] "CD4 Lymph nodes CA Exp01" "CD4 Lymph nodes CA Exp02"
## [3] "CD4 Lymph nodes CA Exp03" "CD4 Lymph nodes CAKR Exp02"
## [5] "CD4 Lymph nodes CAKR Exp03" "CD4 Lymph nodes WT Exp02"
## [7] "CD4 Lymph nodes WT Exp03" "CD4 Thymus CA Exp01"
## [9] "CD4 Thymus CA Exp03" "CD4 Thymus CAKR Exp01"
## [11] "CD4 Thymus CAKR Exp02" "CD4 Thymus CAKR Exp03"
## [13] "CD4 Thymus WT Exp02" "CD4 Thymus WT Exp03"
## [15] "CD8 Lymph nodes CA Exp02" "CD8 Lymph nodes CA Exp03"
## [17] "CD8 Lymph nodes CAKR Exp02" "CD8 Lymph nodes CAKR Exp03"
## [19] "CD8 Lymph nodes WT Exp01" "CD8 Lymph nodes WT Exp02"
## [21] "CD8 Lymph nodes WT Exp03" "CD8 Thymus CA Exp01"
## [23] "CD8 Thymus CA Exp03" "CD8 Thymus CAKR Exp02"
## [25] "CD8 Thymus CAKR Exp03" "CD8 Thymus WT Exp01"
## [27] "CD8 Thymus WT Exp02" "CD8 Thymus WT Exp03"
## [29] "aaSeqCDR3" "allVHitsWithScore"
## [31] "allDHitsWithScore" "allJHitsWithScore"
prop.table.trb2_filter.cd8.thy <- prop.table.trb_filter[,26:32]
prop.table.trb2_filter.cd8.thy$sum <- rowSums(prop.table.trb2_filter.cd8.thy)## Error in rowSums(prop.table.trb2_filter.cd8.thy): 'x' must be numeric
prop.table.trb2_filter.cd8.thy <- prop.table.trb2_filter.cd8.thy %>% filter(sum >0) %>% select(-sum)## Error in `filter()`:
## ! Problem while computing `..1 = sum > 0`.
## Caused by error in `sum > 0`:
## ! comparison (6) is possible only for atomic and list types
res.pca.trb.cd8.thy <- prcomp(t(prop.table.trb2_filter.cd8.thy), scale = TRUE, center = T)## Error in colMeans(x, na.rm = TRUE): 'x' must be numeric
mdres.pca.trb.cd8.thy <- colnames(prop.table.trb_filter[,26:32]) %>%
as.data.frame() %>%
mutate(sample = stringr::str_replace_all(., pattern = "Lymph nodes", replacement = "LN")) %>%
separate(sample, into = c("Cell_type","Organ","Strain","Exp"))
fviz_pca_ind(res.pca.trb.cd8.thy,
col.ind = as.factor(mdres.pca.trb.cd8.thy$Strain), # color by groups
legend.title = "Groups",
geom.ind = "point",
invisible="quali", pointsize = 0) +
scale_color_manual(values=c("dodgerblue","indianred2","gray40")) +
geom_point(aes(shape = as.factor(mdres.pca.trb.cd8.thy$Organ),
color = as.factor(mdres.pca.trb.cd8.thy$Strain))) +
geom_hline(yintercept = 0, color = "grey20") +
geom_vline(xintercept = 0, color = "grey20") ## Error in .get_facto_class(X): object 'res.pca.trb.cd8.thy' not found
#ggsave("final_fig/pca/cd8_trb_thy.png", width = 2.6, height = 1.7)
#ggsave("final_fig/pca/cd8_trb_thy.svg", width = 2.6, height = 1.7)## LN
prop.table.trb_filter %>% colnames## [1] "CD4 Lymph nodes CA Exp01" "CD4 Lymph nodes CA Exp02"
## [3] "CD4 Lymph nodes CA Exp03" "CD4 Lymph nodes CAKR Exp02"
## [5] "CD4 Lymph nodes CAKR Exp03" "CD4 Lymph nodes WT Exp02"
## [7] "CD4 Lymph nodes WT Exp03" "CD4 Thymus CA Exp01"
## [9] "CD4 Thymus CA Exp03" "CD4 Thymus CAKR Exp01"
## [11] "CD4 Thymus CAKR Exp02" "CD4 Thymus CAKR Exp03"
## [13] "CD4 Thymus WT Exp02" "CD4 Thymus WT Exp03"
## [15] "CD8 Lymph nodes CA Exp02" "CD8 Lymph nodes CA Exp03"
## [17] "CD8 Lymph nodes CAKR Exp02" "CD8 Lymph nodes CAKR Exp03"
## [19] "CD8 Lymph nodes WT Exp01" "CD8 Lymph nodes WT Exp02"
## [21] "CD8 Lymph nodes WT Exp03" "CD8 Thymus CA Exp01"
## [23] "CD8 Thymus CA Exp03" "CD8 Thymus CAKR Exp02"
## [25] "CD8 Thymus CAKR Exp03" "CD8 Thymus WT Exp01"
## [27] "CD8 Thymus WT Exp02" "CD8 Thymus WT Exp03"
## [29] "aaSeqCDR3" "allVHitsWithScore"
## [31] "allDHitsWithScore" "allJHitsWithScore"
prop.table.trb2_filter.cd8.ln <- prop.table.trb_filter[,19:25]
prop.table.trb2_filter.cd8.ln$sum <- rowSums(prop.table.trb2_filter.cd8.ln)
prop.table.trb2_filter.cd8.ln <- prop.table.trb2_filter.cd8.ln %>% filter(sum >0) %>% select(-sum)
res.pca.trb.cd8.ln <- prcomp(t(prop.table.trb2_filter.cd8.ln), scale = TRUE, center = T)
mdres.pca.trb.cd8.ln <- colnames(prop.table.trb_filter[,19:25]) %>%
as.data.frame() %>%
mutate(sample = stringr::str_replace_all(., pattern = "Lymph nodes", replacement = "LN")) %>%
separate(sample, into = c("Cell_type","Organ","Strain","Exp"))
fviz_pca_ind(res.pca.trb.cd8.ln,
col.ind = as.factor(mdres.pca.trb.cd8.ln$Strain), # color by groups
legend.title = "Groups",
geom.ind = "point",
invisible="quali", pointsize = 0) +
scale_color_manual(values=c("dodgerblue","indianred2","gray40")) +
geom_point(aes(shape = as.factor(mdres.pca.trb.cd8.ln$Organ),
color = as.factor(mdres.pca.trb.cd8.ln$Strain))) +
geom_hline(yintercept = 0, color = "grey20") +
geom_vline(xintercept = 0, color = "grey20") #ggsave("final_fig/pca/cd8_trb_ln.png", width = 2.6, height = 1.7)
#ggsave("final_fig/pca/cd8_trb_ln.svg", width = 2.6, height = 1.7)To compare the diversity of the TCR repertoires of our samples, we
will calculate the Chao1 index using the Immunarch package.
Chao1 is a nonparameteric asymptotic estimator of species richness. The
higher the Chao1 value, the more diverse repertoire in the sample.
We will calculate the diversity index of TCRa chains without iNKT chains, so we will first exclude them.
# Read metadata
md <- read.csv("metadata_Lck.csv")
# Create filtered dataset
immdata_tra_wonkt <- repFilter(immdata_tra, "by.clonotype",
list(V.name = exclude("TRAV11"), J.name = exclude("TRAJ18")),
.match = "substring")## ALL TRA
repDiversity(immdata_tra_wonkt$data) %>% vis(.by = c( "Mouse_strain"), .meta = immdata_tra$meta,
.signif.label.size = 0) + ggtitle("TRA without NKT") + scale_fill_manual(values = c("dodgerblue","indianred2","gray70")) + ggtitle("all TRA", subtitle = "")# source data
write.csv(repDiversity(immdata_tra_wonkt$data), "rep_diversity_tra.csv")
#ggsave("./final_fig/diversity/chao_tra_wonkt_sign.png", width = 8, height = 13, units = "cm")
#ggsave("./final_fig/diversity/chao_tra_wonkt_sign.svg", width = 8, height = 13, units = "cm")The TCRb repertoire doesn’t need any filtering, so we can use it as it is.
## ALL TRB
repDiversity(immdata_trb$data) %>% vis(.by = c( "Mouse_strain"), .meta = immdata_trb$meta,
.signif.label.size = 0) + ggtitle("TRB without NKT") + scale_fill_manual(values = c("dodgerblue","indianred2","gray70")) + ggtitle("all TRB", subtitle = "")# source data
write.csv(repDiversity(immdata_trb$data), "rep_diversity_trb.csv")
#ggsave("./final_fig/diversity/chao_trb_sign.png", width = 8, height = 13, units = "cm")
#ggsave("./final_fig/diversity/chao_trb_sign.svg", width = 8, height = 13, units = "cm")Next, we divide samples by tissue and by cell type (CD4 versus CD8).
## Chao index
repDiversity(immdata_tra_wonkt$data) %>% vis(.by = c("Cell_type", "Organ","Mouse_strain"), .meta = immdata_tra$meta, .test = F,
.signif.label.size = 0) + ggtitle("TRA")#ggsave("./plots/chao_tra.png", width = 15, height = 11, units = "cm")
#ggsave("./plots/chao_tra.svg", width = 15, height = 11, units = "cm")
repDiversity(immdata_trb$data) %>% vis(.by = c("Cell_type", "Organ","Mouse_strain"), .meta = immdata_trb$meta, .test = F,
.signif.label.size = 0) + ggtitle("TRB", subtitle = "")#ggsave("./plots/chao_trb.png", width = 15, height = 11, units = "cm")
#ggsave("./plots/chao_trb.svg", width = 15, height = 11, units = "cm")chao_tra <- as.data.frame(repDiversity(immdata_tra_wonkt$data))
chao_trb <- as.data.frame(repDiversity(immdata_trb$data))chao_tra2 <- chao_tra %>%
rownames_to_column("num_id") %>%
mutate(num_id = str_replace_all(string = num_id, c("new_lib_24" = "lib24_S24_L001", "new_lib_25" = "lib25_S25_L001"))) %>%
separate(num_id, into = c("num_id",NA,NA), sep = "_") %>%
mutate(num_id = str_replace(num_id, "lib","")) %>%
mutate_at(vars("num_id"), as.numeric)
levels_cd4 <- c("CD4 Thymus WT", "CD4 Thymus CA", "CD4 Thymus CAKR", "CD4 Lymph nodes WT", "CD4 Lymph nodes CA", "CD4 Lymph nodes CAKR")
# Attach metadata sent for sequencing
chao_tra2 <- left_join(chao_tra2, md) %>% select(value = Estimator, Cell_type, Organ, Mouse_strain) %>%
mutate(sample_type = paste(Cell_type, Organ, Mouse_strain))
chao_tra2 %>% filter(Cell_type == "CD4") %>%
ggplot(aes(y = value, x = factor(sample_type, levels = levels_cd4))) +
stat_summary(fun = "mean",
geom = "crossbar",
width = 0.5,
colour = "black") +
geom_point(aes(color = Mouse_strain)) +
ylab("Chao index estimate") +
ggtitle("CD4 TRA") +
theme(axis.text.x = element_text(angle = 90)) +
theme_classic() +
ggtheme() +
theme(axis.text.x = element_text(angle = 90)) +
scale_color_manual(values = c("dodgerblue","indianred2","gray40")) +
ylim(c(0,NA)) + xlab("")#ggsave("./final_fig/diversity/chao_cd4_tra.png", width = 8, height = 10, units = "cm")
#ggsave("./final_fig/diversity/chao_cd4_tra.eps", width = 8, height = 10, units = "cm")
levels_cd8 <- c("CD8 Thymus WT", "CD8 Thymus CA", "CD8 Thymus CAKR", "CD8 Lymph nodes WT", "CD8 Lymph nodes CA", "CD8 Lymph nodes CAKR")
chao_tra2 %>% filter(Cell_type == "CD8") %>%
ggplot(aes(y = value, x = factor(sample_type, levels = levels_cd8))) +
stat_summary(fun = "mean",
geom = "crossbar",
width = 0.5,
colour = "black") +
geom_point(aes(color = Mouse_strain)) +
ylab("Chao index estimate") +
ggtitle("CD8 TRA") +
theme(axis.text.x = element_text(angle = 90)) +
theme_classic() +
ggtheme() +
theme(axis.text.x = element_text(angle = 90)) +
scale_color_manual(values = c("dodgerblue","indianred2","gray40")) +
ylim(c(0,NA)) + xlab("")#ggsave("./final_fig/diversity/chao_cd8_tra.png", width = 8, height = 10, units = "cm")
#ggsave("./final_fig/diversity/chao_cd8_tra.eps", width = 8, height = 10, units = "cm")chao_trb2 <- chao_trb %>%
rownames_to_column("num_id") %>% mutate(num_id = str_replace_all(string = num_id, c("new_lib_24" = "lib24_S24_L001", "new_lib_25" = "lib25_S25_L001"))) %>%
separate(num_id, into = c("num_id",NA,NA), sep = "_") %>%
mutate(num_id = str_replace(num_id, "lib","")) %>%
mutate_at(vars("num_id"), as.numeric)
levels_cd4 <- c("CD4 Thymus WT", "CD4 Thymus CA", "CD4 Thymus CAKR", "CD4 Lymph nodes WT", "CD4 Lymph nodes CA", "CD4 Lymph nodes CAKR")
chao_trb2 <- left_join(chao_trb2, md) %>% select(value = Estimator, Cell_type, Organ, Mouse_strain) %>%
mutate(sample_type = paste(Cell_type, Organ, Mouse_strain))
chao_trb2 %>% filter(Cell_type == "CD4") %>%
ggplot(aes(y = value, x = factor(sample_type, levels = levels_cd4))) +
stat_summary(fun = "mean",
geom = "crossbar",
width = 0.5,
colour = "black") +
geom_point(aes(color = Mouse_strain)) +
ylab("Chao index estimate") +
ggtitle("CD4 TRB") +
theme(axis.text.x = element_text(angle = 90)) +
theme_classic() +
ggtheme() +
theme(axis.text.x = element_text(angle = 90)) +
scale_color_manual(values = c("dodgerblue","indianred2","gray40")) +
ylim(c(0,125500)) + xlab("")#ggsave("./final_fig/diversity/chao_cd4_trb.png", width = 8, height = 10, units = "cm")
#ggsave("./final_fig/diversity/chao_cd4_trb.eps", width = 8, height = 10, units = "cm")
levels_cd8 <- c("CD8 Thymus WT", "CD8 Thymus CA", "CD8 Thymus CAKR", "CD8 Lymph nodes WT", "CD8 Lymph nodes CA", "CD8 Lymph nodes CAKR")
chao_trb2 %>% filter(Cell_type == "CD8") %>%
ggplot(aes(y = value, x = factor(sample_type, levels = levels_cd8))) +
stat_summary(fun = "mean",
geom = "crossbar",
width = 0.5,
colour = "black") +
geom_point(aes(color = Mouse_strain)) +
ylab("Chao index estimate") +
ggtitle("CD8 TRB") +
theme(axis.text.x = element_text(angle = 90)) +
theme_classic() +
ggtheme() +
theme(axis.text.x = element_text(angle = 90)) +
scale_color_manual(values = c("dodgerblue","indianred2","gray40")) +
ylim(c(0,125500)) + xlab("")#ggsave("./final_fig/diversity/chao_cd8_trb.png", width = 8, height = 10, units = "cm")
#ggsave("./final_fig/diversity/chao_cd8_trb.eps", width = 8, height = 10, units = "cm")chao_trb2 %>%
ggplot(aes(y = value, x = factor(Mouse_strain, levels = c("WT","CA","CAKR")))) +
stat_summary(fun = "mean",
geom = "crossbar",
width = 0.5,
colour = "black") +
geom_dotplot(binaxis='y', stackdir='center', dotsize=0) +
geom_jitter(position=position_jitter(0.1), size = 2, aes(color = Mouse_strain)) +
ylab("Chao index estimate") +
theme(axis.text.x = element_text(angle = 90)) +
theme_classic() +
ggtheme() +
theme(axis.text.x = element_text(angle = 90)) +
scale_color_manual(values = c("dodgerblue","indianred2","gray40")) +
ylim(c(0,NA)) + xlab("")#ggsave("./final_fig/diversity/chao_all_trb.png", width = 8, height = 6, units = "cm")
#ggsave("./final_fig/diversity/chao_all_trb.eps", width = 8, height = 6, units = "cm")
chao_tra2 %>%
ggplot(aes(y = value, x = factor(Mouse_strain, levels = c("WT","CA","CAKR")))) +
stat_summary(fun = "mean",
geom = "crossbar",
width = 0.5,
colour = "black") +
geom_dotplot(binaxis='y', stackdir='center', dotsize=0) +
geom_jitter(position=position_jitter(0.1), size = 2, aes(color = Mouse_strain)) +
ylab("Chao index estimate") +
theme(axis.text.x = element_text(angle = 90)) +
theme_classic() +
ggtheme() +
theme(axis.text.x = element_text(angle = 90)) +
scale_color_manual(values = c("dodgerblue","indianred2","gray40")) +
ylim(c(0,NA)) + xlab("")#ggsave("./final_fig/diversity/chao_all_tra.png", width = 8, height = 6, units = "cm")
#ggsave("./final_fig/diversity/chao_all_tra.eps", width = 8, height = 6, units = "cm")In the next analyses, we will figure out whether the sequences that are abundant in the periphery of WT animals are also present in LckKO animals. We will select the top 20 most abundant sequences from the lymph nodes of WT animals by calculating the average normalized frequency:
## TRA
topclones <- prop.table.tra_filter %>%
mutate(topclones_ln_cd4 = (`CD4 Lymph nodes WT Exp02`+
`CD4 Lymph nodes WT Exp03`)/2,
topclones_ln_cd8 = (`CD8 Lymph nodes WT Exp01`+
`CD8 Lymph nodes WT Exp02`+
`CD8 Lymph nodes WT Exp03`)/3)
cd4_topclones_20sequences_tra <-
pull(topclones %>% slice_max(order_by = topclones_ln_cd4, n = 20),
aaSeqCDR3)
cd8_topclones_20sequences_tra <-
pull(topclones %>% slice_max(order_by = topclones_ln_cd8, n = 20),
aaSeqCDR3)
## TRB
topclones <- prop.table.trb_filter %>%
mutate(topclones_ln_cd4 = (`CD4 Lymph nodes WT Exp02`+
`CD4 Lymph nodes WT Exp03`)/2,
topclones_ln_cd8 = (`CD8 Lymph nodes WT Exp01`+
`CD8 Lymph nodes WT Exp02`+
`CD8 Lymph nodes WT Exp03`)/3)
cd4_topclones_20sequences_trb <- pull(topclones %>%
slice_max(order_by = topclones_ln_cd4, n = 20), aaSeqCDR3)
cd8_topclones_20sequences_trb <- pull(topclones %>%
slice_max(order_by = topclones_ln_cd8, n = 20), aaSeqCDR3)Top 20 most abundant TRA sequences in CD4 WT LNs:
cd4_topclones_20sequences_tra## [1] "CAASANSGTYQRF" "CAASASSGSWQLIF" "CAAASSGSWQLIF" "CAASDYSNNRLTL"
## [5] "CAASDTNTGKLTF" "CAASRNSNNRIFF" "CAASDQGGRALIF" "CAASNTNTGKLTF"
## [9] "CAASPNTNKVVF" "CAASSNTNKVVF" "CAAGTGGYKVVF" "CAASITGNTGKLIF"
## [13] "CAASDTNAYKVIF" "CAASSSGSWQLIF" "CAATGNTGKLIF" "CAASGGSNAKLTF"
## [17] "CAASGTGGYKVVF" "CAASSGSWQLIF" "CAARSSGSWQLIF" "CAARNSNNRIFF"
Top 20 most abundant TRB sequences in CD4 WT LNs:
cd4_topclones_20sequences_trb## [1] "CASSLGQNTEVFF" "CASSGTANSDYTF" "CASSLDSQNTLYF" "CASSRDNYAEQFF"
## [5] "CASSQGQGSYEQYF" "CASSPTGVQDTQYF" "CAWSLGGQDTQYF" "CASSRDWGYEQYF"
## [9] "CASSRDSYAEQFF" "CASSRDSQNTLYF" "CASSLGGQNTLYF" "CASSLGSQNTLYF"
## [13] "CASSLSQNTLYF" "CASSLQGNTEVFF" "CASGDEQYF" "CASGDAGGQNTLYF"
## [17] "CASSGTANTEVFF" "CASSQEGGGTEVFF" "CASSRQGNTEVFF" "CASSPSSYEQYF"
Top 20 most abundant TRA sequences in CD8 WT LNs:
cd8_topclones_20sequences_tra## [1] "CAASASSGSWQLIF" "CALSDRYNQGKLIF" "CAASNMGYKLTF" "CAVSASSGSWQLIF"
## [5] "CAVDTNAYKVIF" "CAAASSGSWQLIF" "CAASSGSWQLIF" "CAASDNYAQGLTF"
## [9] "CAASDTNAYKVIF" "CAADTNAYKVIF" "CAVSNMGYKLTF" "CAASGTGGYKVVF"
## [13] "CAAGATGGNNKLTF" "CALMGYKLTF" "CAASANSGTYQRF" "CALGDTNAYKVIF"
## [17] "CAASDDTNAYKVIF" "CAASDMGYKLTF" "CAASAGTGGYKVVF" "CAMREGSSGSWQLIF"
Top 20 most abundant TRB sequences in CD8 WT LNs:
cd8_topclones_20sequences_trb## [1] "CASSDAGYEQYF" "CASSPGTGGYEQYF" "CASGDAGEQYF" "CASGDAGGSAETLYF"
## [5] "CASSDSAETLYF" "CASSRDNYAEQFF" "CASSPGQQDTQYF" "CASSLGYEQYF"
## [9] "CASSLGAEQFF" "CASSPGQYAEQFF" "CASSLGGNYAEQFF" "CASSDWGNYAEQFF"
## [13] "CASSLGANSDYTF" "CASSPGQNYAEQFF" "CASSLGGQDTQYF" "CASGDAGYEQYF"
## [17] "CASSLGNYAEQFF" "CASSRDNYEQYF" "CASSDSYEQYF" "CASSLDNYAEQFF"
Now we will investigate what is the percentage of the repertoire of each sample that is occupied by the top20 WT LN sequences that we identified earlier. We will plot the results.
cd4_topclones_rep_pct <- prop.table.tra2 %>%
filter(aaSeqCDR3 %in% cd4_topclones_20sequences_tra) %>%
select(aaSeqCDR3, starts_with("CD4")) %>%
select(1,14,15,9:13,7,8,2:6)
cd4_topclones_rep_pct2 <- as.data.frame(colSums(cd4_topclones_rep_pct[,2:15]))
colnames(cd4_topclones_rep_pct2) <- "value"
cd4_topclones_rep_pct2 <- cd4_topclones_rep_pct2 %>%
rownames_to_column("sample") %>%
mutate(sample = stringr::str_replace_all(sample, pattern = "Lymph nodes", replacement = "LN")) %>%
separate(sample, into = c("Cell_type","Organ","Strain","Exp")) %>%
mutate(Cell_type_organ = factor(paste(Organ, Strain),
levels = c("Thymus WT", "Thymus CA", "Thymus CAKR",
"LN WT", "LN CA", "LN CAKR")))
cd4_topclones_rep_pct2 %>%
filter(Organ == "LN") %>%
ggplot(aes(y = value*100, x = factor(Cell_type_organ))) +
stat_summary(fun = "mean",
geom = "crossbar",
width = 0.5,
colour = "black") +
geom_point(aes(color = Strain)) +
ylab("% of repertoire") +
ggtitle("CD4 TRA") +
theme(axis.text.x = element_text(angle = 90)) +
theme_classic() +
ggtheme() +
theme(axis.text.x = element_text(angle = 90)) +
scale_color_manual(values = c("dodgerblue","indianred2","gray40")) +
ylim(c(0,NA)) + xlab("")# source data
write.csv(cd4_topclones_rep_pct2 %>%
filter(Organ == "LN") %>%
mutate(value = value*100),
file = "topclones_cd4_tra.csv")
#ggsave("./final_fig/20topseqs/20topseqs_pct_tra_cd4.png", width = 5, height = 5.7, units = "cm")
#ggsave("./final_fig/20topseqs/20topseqs_pct_tra_cd4.eps", width = 5, height = 5.7, units = "cm")cd4_topclones_rep_pct <- prop.table.trb2 %>%
filter(aaSeqCDR3 %in% cd4_topclones_20sequences_trb) %>%
select(aaSeqCDR3, starts_with("CD4")) %>%
select(1,14,15,9:13,7,8,2:6)
cd4_topclones_rep_pct2 <- as.data.frame(colSums(cd4_topclones_rep_pct[,2:15]))
colnames(cd4_topclones_rep_pct2) <- "value"
cd4_topclones_rep_pct2 <- cd4_topclones_rep_pct2 %>%
rownames_to_column("sample") %>%
mutate(sample = stringr::str_replace_all(sample, pattern = "Lymph nodes", replacement = "LN")) %>%
separate(sample, into = c("Cell_type","Organ","Strain","Exp")) %>%
mutate(Cell_type_organ = factor(paste(Organ, Strain), levels = c("Thymus WT","Thymus CA", "Thymus CAKR",
"LN WT","LN CA", "LN CAKR" )))
cd4_topclones_rep_pct2 %>%
filter(Organ == "LN") %>%
ggplot(aes(y = value*100, x = factor(Cell_type_organ))) +
stat_summary(fun = "mean",
geom = "crossbar",
width = 0.5,
colour = "black") +
geom_point(aes(color = Strain)) +
ylab("% of repertoire") +
ggtitle("CD4 TRB") +
theme(axis.text.x = element_text(angle = 90)) +
theme_classic() +
ggtheme() +
theme(axis.text.x = element_text(angle = 90)) +
scale_color_manual(values = c("dodgerblue","indianred2","gray40")) +
ylim(c(0,NA)) + xlab("")# source data
write.csv(cd4_topclones_rep_pct2 %>%
filter(Organ == "LN") %>%
mutate(value = value*100),
file = "topclones_cd4_trb.csv")
#ggsave("./final_fig/20topseqs/20topseqs_pct_trb_cd4.png", width = 5.4, height = 5.7, units = "cm")
#ggsave("./final_fig/20topseqs/20topseqs_pct_trb_cd4.eps", width = 5.4, height = 5.7, units = "cm")cd8_topclones_rep_pct <- prop.table.tra2 %>%
filter(aaSeqCDR3 %in% cd8_topclones_20sequences_tra) %>%
select(aaSeqCDR3, starts_with("CD8")) %>%
select(1,13:15,9:12,6:8,2:5)
cd8_topclones_rep_pct2 <- as.data.frame(colSums(cd8_topclones_rep_pct[,2:15]))
colnames(cd8_topclones_rep_pct2) <- "value"
cd8_topclones_rep_pct2 <- cd8_topclones_rep_pct2 %>%
rownames_to_column("sample") %>%
mutate(sample = stringr::str_replace_all(sample, pattern = "Lymph nodes", replacement = "LN")) %>%
separate(sample, into = c("Cell_type","Organ","Strain","Exp")) %>%
mutate(Cell_type_organ = factor(paste(Organ, Strain), levels = c("Thymus WT","Thymus CA", "Thymus CAKR",
"LN WT","LN CA", "LN CAKR" )))
cd8_topclones_rep_pct2## Cell_type Organ Strain Exp value Cell_type_organ
## 1 CD8 Thymus WT Exp01 0.03400861 Thymus WT
## 2 CD8 Thymus WT Exp02 0.02653846 Thymus WT
## 3 CD8 Thymus WT Exp03 0.03137384 Thymus WT
## 4 CD8 Thymus CA Exp01 0.02447936 Thymus CA
## 5 CD8 Thymus CA Exp03 0.02870174 Thymus CA
## 6 CD8 Thymus CAKR Exp02 0.03063281 Thymus CAKR
## 7 CD8 Thymus CAKR Exp03 0.02280437 Thymus CAKR
## 8 CD8 LN WT Exp01 0.03753115 LN WT
## 9 CD8 LN WT Exp02 0.03764212 LN WT
## 10 CD8 LN WT Exp03 0.03642344 LN WT
## 11 CD8 LN CA Exp02 0.03592586 LN CA
## 12 CD8 LN CA Exp03 0.03213014 LN CA
## 13 CD8 LN CAKR Exp02 0.02946860 LN CAKR
## 14 CD8 LN CAKR Exp03 0.03059997 LN CAKR
cd8_topclones_rep_pct2 %>%
filter(Organ == "LN") %>%
ggplot(aes(y = value*100, x = factor(Cell_type_organ))) +
stat_summary(fun = "mean",
geom = "crossbar",
width = 0.5,
colour = "black") +
geom_point(aes(color = Strain)) +
ylab("% of repertoire") +
ggtitle("CD8 TRA") +
theme(axis.text.x = element_text(angle = 90)) +
theme_classic() +
ggtheme() +
theme(axis.text.x = element_text(angle = 90)) +
scale_color_manual(values = c("dodgerblue","indianred2","gray40")) +
ylim(c(0,NA)) + xlab("")# source data
write.csv(cd8_topclones_rep_pct2 %>%
filter(Organ == "LN") %>%
mutate(value = value*100),
file = "topclones_cd8_tra.csv")
#ggsave("./final_fig/20topseqs/20topseqs_pct_tra_cd8.png", width = 5, height = 5.7, units = "cm")
#ggsave("./final_fig/20topseqs/20topseqs_pct_tra_cd8.eps", width = 5, height = 5.7, units = "cm")cd8_topclones_rep_pct <- prop.table.trb2 %>%
filter(aaSeqCDR3 %in% cd8_topclones_20sequences_trb) %>%
select(aaSeqCDR3, starts_with("CD8")) %>%
select(1,13:15,9:12,6:8,2:5)
cd8_topclones_rep_pct2 <- as.data.frame(colSums(cd8_topclones_rep_pct[,2:15]))
colnames(cd8_topclones_rep_pct2) <- "value"
cd8_topclones_rep_pct2 <- cd8_topclones_rep_pct2 %>%
rownames_to_column("sample") %>%
mutate(sample = stringr::str_replace_all(sample, pattern = "Lymph nodes", replacement = "LN")) %>%
separate(sample, into = c("Cell_type","Organ","Strain","Exp")) %>%
mutate(Cell_type_organ = factor(paste(Organ, Strain), levels = c("Thymus WT","Thymus CA", "Thymus CAKR",
"LN WT","LN CA", "LN CAKR" )))
cd8_topclones_rep_pct2## Cell_type Organ Strain Exp value Cell_type_organ
## 1 CD8 Thymus WT Exp01 0.004631440 Thymus WT
## 2 CD8 Thymus WT Exp02 0.004479484 Thymus WT
## 3 CD8 Thymus WT Exp03 0.004929262 Thymus WT
## 4 CD8 Thymus CA Exp01 0.005472183 Thymus CA
## 5 CD8 Thymus CA Exp03 0.004517467 Thymus CA
## 6 CD8 Thymus CAKR Exp02 0.003983029 Thymus CAKR
## 7 CD8 Thymus CAKR Exp03 0.005094131 Thymus CAKR
## 8 CD8 LN WT Exp01 0.007470700 LN WT
## 9 CD8 LN WT Exp02 0.006188663 LN WT
## 10 CD8 LN WT Exp03 0.007226656 LN WT
## 11 CD8 LN CA Exp02 0.006775749 LN CA
## 12 CD8 LN CA Exp03 0.007135684 LN CA
## 13 CD8 LN CAKR Exp02 0.007553967 LN CAKR
## 14 CD8 LN CAKR Exp03 0.006905450 LN CAKR
cd8_topclones_rep_pct2 %>%
filter(Organ == "LN") %>%
ggplot(aes(y = value*100, x = factor(Cell_type_organ))) +
stat_summary(fun = "mean",
geom = "crossbar",
width = 0.5,
colour = "black") +
geom_point(aes(color = Strain)) +
ylab("% of repertoire") +
ggtitle("CD8 TRB") +
theme(axis.text.x = element_text(angle = 90)) +
theme_classic() +
ggtheme() +
theme(axis.text.x = element_text(angle = 90)) +
scale_color_manual(values = c("dodgerblue","indianred2","gray40")) +
ylim(c(0,NA)) + xlab("")# source data
write.csv(cd8_topclones_rep_pct2 %>%
filter(Organ == "LN") %>%
mutate(value = value*100),
file = "topclones_cd8_trb.csv")
#ggsave("./final_fig/20topseqs/20topseqs_pct_trb_cd8.png", width = 5.4, height = 5.7, units = "cm")
#ggsave("./final_fig/20topseqs/20topseqs_pct_trb_cd8.eps", width = 5.4, height = 5.7, units = "cm")In this part, we will use heatmaps to visualize the frequency of the sequences that were the most abundant (top20) in the periphery of WT mice in all samples. In the supplementary figure in the manuscript, reference samples (periphery of WT mice) are highlighted with black border, which was added manually in a graphics editor.
Heatmap showing the frequency of the top20 CD4 TRA sequences from WT LNs in all samples.
# TRA
cd4_topclones_heatmap_matrix <- prop.table.tra2 %>%
filter(aaSeqCDR3 %in% cd4_topclones_20sequences_tra) %>%
select(aaSeqCDR3, starts_with("CD4")) %>%
select(1,14,15,9:13,7,8,2:6)
cd4_topclones_heatmap_matrix2 <- as.matrix(cd4_topclones_heatmap_matrix[,2:15])
rownames(cd4_topclones_heatmap_matrix2) <- cd4_topclones_heatmap_matrix$aaSeqCDR3
cd4_topclones_heatmap_matrix2 <- cd4_topclones_heatmap_matrix2[match(cd4_topclones_20sequences_tra, rownames(cd4_topclones_heatmap_matrix2)),]
pheatmap(cd4_topclones_heatmap_matrix2, scale = "none", cluster_rows = F, cluster_cols = F)#ggsave(plot = pheatmap(cd4_topclones_heatmap_matrix2, scale = "none", cluster_rows = F, cluster_cols = F), "final_fig/heatmap_top20_ordered/cd4_tra_noscale.svg")Heatmap showing the frequency of the top20 CD8 TRA sequences from WT LNs in all samples.
# TRA
cd8_topclones_heatmap_matrix <- prop.table.tra2 %>%
filter(aaSeqCDR3 %in% cd8_topclones_20sequences_tra) %>%
select(aaSeqCDR3, starts_with("CD8")) %>%
select(1,13:15,9:12,6:8,2:5)
cd8_topclones_heatmap_matrix2 <- as.matrix(cd8_topclones_heatmap_matrix[,2:15])
rownames(cd8_topclones_heatmap_matrix2) <- cd8_topclones_heatmap_matrix$aaSeqCDR3
cd8_topclones_heatmap_matrix2 <- cd8_topclones_heatmap_matrix2[match(cd8_topclones_20sequences_tra, rownames(cd8_topclones_heatmap_matrix2)),]
pheatmap(cd8_topclones_heatmap_matrix2, scale = "none", cluster_rows = F, cluster_cols = F)#ggsave(plot = pheatmap(cd8_topclones_heatmap_matrix2, scale = "none", cluster_rows = F, cluster_cols = F), "final_fig/heatmap_top20_ordered/cd8_tra_noscale.svg")As we can see that the first sequence takes up the most percentage of the repertoire, we will also plot another heatmap without the first sequence.
### without 1st sequence
cd8_topclones_heatmap_matrix2 <- as.matrix(cd8_topclones_heatmap_matrix[2:20,2:15])
rownames(cd8_topclones_heatmap_matrix2) <- cd8_topclones_heatmap_matrix$aaSeqCDR3[2:20]
cd8_topclones_heatmap_matrix2 <- cd8_topclones_heatmap_matrix2[match(cd8_topclones_20sequences_tra[2:20], rownames(cd8_topclones_heatmap_matrix2)),]
pheatmap(cd8_topclones_heatmap_matrix2, scale = "none", cluster_rows = F, cluster_cols = F)#ggsave(plot = pheatmap(cd8_topclones_heatmap_matrix2, scale = "none", cluster_rows = F, cluster_cols = F), "final_fig/heatmap_top20_ordered/cd8_tra_noscale_without.svg")Heatmap showing the frequency of the top20 CD4 TRB sequences from WT LNs in all samples.
# trb
cd4_topclones_heatmap_matrix <- prop.table.trb2 %>% filter(aaSeqCDR3 %in% cd4_topclones_20sequences_trb) %>% select(aaSeqCDR3, starts_with("CD4")) %>% select(1,14,15,9:13,7,8,2:6)
cd4_topclones_heatmap_matrix2 <- as.matrix(cd4_topclones_heatmap_matrix[,2:15])
rownames(cd4_topclones_heatmap_matrix2) <- cd4_topclones_heatmap_matrix$aaSeqCDR3
cd4_topclones_heatmap_matrix2 <- cd4_topclones_heatmap_matrix2[match( cd4_topclones_20sequences_trb, rownames(cd4_topclones_heatmap_matrix2)),]
pheatmap(cd4_topclones_heatmap_matrix2, scale = "none", cluster_rows = F, cluster_cols = F)#ggsave(plot = pheatmap(cd4_topclones_heatmap_matrix2, scale = "none", cluster_rows = F, cluster_cols = F), "final_fig/heatmap_top20_ordered/cd4_trb_noscale.svg")Heatmap showing the frequency of the top20 CD8 TRB sequences from WT LNs in all samples.
# trb
cd8_topclones_heatmap_matrix <- prop.table.trb2 %>% filter(aaSeqCDR3 %in% cd8_topclones_20sequences_trb) %>% select(aaSeqCDR3, starts_with("CD8")) %>% select(1,13:15,9:12,6:8,2:5)
cd8_topclones_heatmap_matrix2 <- as.matrix(cd8_topclones_heatmap_matrix[,2:15])
rownames(cd8_topclones_heatmap_matrix2) <- cd8_topclones_heatmap_matrix$aaSeqCDR3
cd8_topclones_heatmap_matrix2 <- cd8_topclones_heatmap_matrix2[match( cd8_topclones_20sequences_trb, rownames(cd8_topclones_heatmap_matrix2)),]
pheatmap(cd8_topclones_heatmap_matrix2, scale = "none", cluster_rows = F, cluster_cols = F)# png("final_fig/heatmap_top20_ordered/cd8_trb_noscale.png", width = 400, height = 500)
# pheatmap(cd8_topclones_heatmap_matrix2, scale = "none", cluster_rows = F, cluster_cols = F)
# dev.off()
#ggsave(plot = pheatmap(cd8_topclones_heatmap_matrix2, scale = "none", cluster_rows = F, cluster_cols = F), "final_fig/heatmap_top20_ordered/cd8_trb_noscale.svg")binary_tra <- excel_count_table_tra %>%
mutate(nkt_trav11_traj18 = if_else(
(grepl(allVHitsWithScore, pattern = "TRAV11") |
(grepl(allJHitsWithScore, pattern = "TRAJ18"))),"yes","no")) %>%
filter(nkt_trav11_traj18 == "no") %>%
mutate_at(vars(starts_with("CD")), .funs = binary)
# count_overlap <- function(df, column_number){
# repertoire <- df %>% select(vars(column_number), aaSeqCDR3) %>% filter(vars(column_number)>0) %>% pull("aaSeqCDR3")
# nrow_column <- length(repertoire)
# intersect_rept <- length(intersect(repertoire, most_diverse_repertoire))
# pct <- intersect_rept/length()
# return(x)
# }
binary_tra_longer <- binary_tra %>% select(starts_with("CD"), aaSeqCDR3) %>% pivot_longer(!aaSeqCDR3, names_to = "num_id")
df_all4 <- data.frame("")
for(j in levels(factor(binary_tra_longer$num_id))){
subset1 <- binary_tra_longer %>% filter(num_id == j & value>0) %>% pull("aaSeqCDR3")
vector_overlap <- c()
for(i in levels(factor(binary_tra_longer$num_id))){
subset2 <- binary_tra_longer %>% filter(num_id == i & value>0) %>% pull("aaSeqCDR3")
intersect_rep <- length(intersect(subset1, subset2))
total <- length(subset2)
vector_overlap <- c(vector_overlap,intersect_rep/total)
}
df <- as.data.frame(x = vector_overlap)
colnames(df) <- j
df
df_all4 <- cbind(df_all4, df)
}
df_all4 <- df_all4[,2:29]
rownames(df_all4) <- colnames(df_all4)
#write.csv(df_all4, "rep_overlap_tra.csv")df24 <- df_all4
df24[df24 == 1] <- 0
### plot dotplot of overlaps
df25 <- df24 %>% rownames_to_column("id") %>% pivot_longer(-id)
g2 <- ggplot(df25, aes(id, factor(name, levels = rev(levels(factor(name)))))) +
geom_point(aes(size = value*100, colour = value*100)) +
theme_bw()
g2 + scale_size_continuous(range=c(7,12)) +
geom_text(aes(label = round(value*100, digits = 1))) +
scale_colour_gradient2(low = "lightskyblue", mid = "lightsteelblue2", high = "salmon") +
theme(axis.text.x = element_text(angle = 90)) rep_overlap_tra <- read_csv("rep_overlap_tra.csv") %>% column_to_rownames("...1")
rep_overlap_tra_cd4 <- rep_overlap_tra[1:14,1:14]
rep_overlap_tra_cd8 <- rep_overlap_tra[15:28,15:28]
rep_overlap_tra_cd4 <- rep_overlap_tra_cd4[ rev(c(5, 4, 3, 2, 1, 7, 6, 12, 11, 10, 9, 8, 14, 13)),
rev(c(5, 4, 3, 2, 1, 7, 6, 12, 11, 10, 9, 8, 14, 13))]
levels_names <- colnames(rep_overlap_tra_cd4)
df_overlap_tra_cd4 <- rep_overlap_tra_cd4
df_overlap_tra_cd4[df_overlap_tra_cd4 == 1] <- 0
### plot dotplot of overlaps
df_overlap_tra_cd4 <- df_overlap_tra_cd4 %>% rownames_to_column("id") %>% pivot_longer(-id)
g2 <- ggplot(df_overlap_tra_cd4, aes(x = factor(name, levels = levels_names), y = factor(id, levels = rev(levels_names)))) +
geom_point(aes(size = value*100, colour = value*100)) +
theme_bw()
g2 + scale_size_continuous(range=c(5,9)) +
geom_text(aes(label = round(value*100, digits = 1)), size = 3) +
scale_colour_gradient2(low = "white", mid = "lightsteelblue", high = "salmon") +
theme(axis.text.x = element_text(angle = 90)) + ggtheme()#ggsave(file = "./final_fig/overlap_cd4_tra.png", width = 20, height = 16, units = "cm")
#ggsave(file = "./final_fig/overlap_cd4_tra.svg", width = 20, height = 16, units = "cm")rep_overlap_tra_cd8 <- rep_overlap_tra_cd8[rev(c(12:14,8:11,5:7,1:4)),rev(c(12:14,8:11,5:7,1:4))]
levels_names <- rev(colnames(rep_overlap_tra_cd8))
df_overlap_tra_cd8 <- rep_overlap_tra_cd8
df_overlap_tra_cd8[df_overlap_tra_cd8 == 1] <- 0
### plot dotplot of overlaps
df_overlap_tra_cd8 <- df_overlap_tra_cd8 %>% rownames_to_column("id") %>% pivot_longer(-id)
g2 <- ggplot(df_overlap_tra_cd8, aes(factor(name, levels = levels_names), factor(id, levels = rev(levels_names)))) +
geom_point(aes(size = value*100, colour = value*100)) +
theme_bw()
g2 + scale_size_continuous(range=c(5,9)) +
geom_text(aes(label = round(value*100, digits = 1)), size = 3) +
scale_colour_gradient2(low = "white", mid = "lightsteelblue", high = "salmon") +
theme(axis.text.x = element_text(angle = 90)) + ggtheme()#ggsave(file = "./final_fig/overlap_cd8_tra.png", width = 20, height = 16, units = "cm")
#ggsave(file = "./final_fig/overlap_cd8_tra.svg", width = 20, height = 16, units = "cm")violin_tra_cd4 <- rep_overlap_tra_cd4 %>%
rownames_to_column("sample1") %>%
pivot_longer(!sample1, names_to = "sample2") %>%
mutate(sample2 = stringr::str_replace_all(sample2, pattern = "Lymph nodes", replacement = "LN")) %>%
mutate(sample1 = stringr::str_replace_all(sample1, pattern = "Lymph nodes", replacement = "LN")) %>%
separate(sample1, into = c("Cell_type1","Organ1","Strain1","Exp1"), remove = F) %>%
separate(sample2, into = c("Cell_type2","Organ2","Strain2","Exp2"), remove = F) %>%
filter(value<1)
violin_tra_cd8 <- rep_overlap_tra_cd8 %>%
rownames_to_column("sample1") %>%
pivot_longer(!sample1, names_to = "sample2") %>%
mutate(sample2 = stringr::str_replace_all(sample2, pattern = "Lymph nodes", replacement = "LN")) %>%
mutate(sample1 = stringr::str_replace_all(sample1, pattern = "Lymph nodes", replacement = "LN")) %>%
separate(sample1, into = c("Cell_type1","Organ1","Strain1","Exp1"), remove = F) %>%
separate(sample2, into = c("Cell_type2","Organ2","Strain2","Exp2"), remove = F) %>%
filter(value<1)violin_tra_cd4 %>% filter(Organ1 == "Thymus" & Organ2 == "Thymus") %>%
mutate(organ_strain1 = paste(Organ1, Strain1),
organ_strain2 = paste(Organ2, Strain2)) %>%
mutate(comparison = paste(organ_strain1, organ_strain2, sep = " - ")) %>%
filter(comparison %in% c("Thymus WT - Thymus WT", "Thymus CA - Thymus WT", "Thymus CAKR - Thymus WT")) %>%
ggplot(aes(x = comparison, y = value)) +
geom_dotplot(binaxis='y', stackdir='center', dotsize=0) +
geom_jitter(position=position_jitter(0.2), size = 2, aes(color = comparison)) +
ggtitle("CD4 TRA Thymus") +
stat_summary(fun = "mean",
geom = "crossbar",
width = 0.5,
colour = "black") +
ylab("% repertoire overlap") +
theme_classic() +
ggtheme() +
theme(axis.text.x = element_text(angle = 90)) +
scale_color_manual(values = c("dodgerblue","indianred2","gray40")) +
ylim(c(0,0.35)) + xlab("")#ggsave("./final_fig/overlaps/rep_overlap_cd4_tra_thy.png", width = 9.5, height = 9.5, units = "cm")
#ggsave("./final_fig/overlaps/rep_overlap_cd4_tra_thy.eps", width = 9.5, height = 9.5, units = "cm")violin_tra_cd4 %>% filter(Organ1 == "LN" & Organ2 == "LN") %>%
mutate(organ_strain1 = paste(Organ1, Strain1),
organ_strain2 = paste(Organ2, Strain2)) %>%
mutate(comparison = paste(organ_strain1, organ_strain2, sep = " - ")) %>%
filter(comparison %in% c("LN WT - LN WT", "LN CA - LN WT", "LN CAKR - LN WT")) %>%
ggplot(aes(x = comparison, y = value)) +
geom_dotplot(binaxis='y', stackdir='center', dotsize=0) +
geom_jitter(position=position_jitter(0.2), size = 2, aes(color = comparison)) +
ggtitle("CD4 TRA LN") +
stat_summary(fun = "mean",
geom = "crossbar",
width = 0.5,
colour = "black") +
ylab("% repertoire overlap") +
theme_classic() +
ggtheme() +
theme(axis.text.x = element_text(angle = 90)) +
scale_color_manual(values = c("dodgerblue","indianred2","gray40")) +
ylim(c(0,0.35)) + xlab("")#ggsave("./final_fig/overlaps/rep_overlap_cd4_tra_LN.png", width = 8, height = 8, units = "cm")
#ggsave("./final_fig/overlaps/rep_overlap_cd4_tra_LN.eps", width = 8, height = 8, units = "cm")violin_tra_cd8 %>% filter(Organ1 == "Thymus" & Organ2 == "Thymus") %>%
mutate(organ_strain1 = paste(Organ1, Strain1),
organ_strain2 = paste(Organ2, Strain2)) %>%
mutate(comparison = paste(organ_strain1, organ_strain2, sep = " - ")) %>%
filter(comparison %in% c("Thymus WT - Thymus WT", "Thymus CA - Thymus WT", "Thymus CAKR - Thymus WT")) %>%
ggplot(aes(x = comparison, y = value)) +
geom_dotplot(binaxis='y', stackdir='center', dotsize=0) +
geom_jitter(position=position_jitter(0.2), size = 2, aes(color = comparison)) +
ggtitle("CD8 TRA Thymus") +
stat_summary(fun = "mean",
geom = "crossbar",
width = 0.5,
colour = "black") +
ylab("% repertoire overlap") +
theme_classic() +
ggtheme() +
theme(axis.text.x = element_text(angle = 90)) +
scale_color_manual(values = c("dodgerblue","indianred2","gray40")) +
ylim(c(0,0.35)) + xlab("")#ggsave("./final_fig/overlaps/rep_overlap_cd8_tra_thy.png", width = 9.5, height = 9.5, units = "cm")
#ggsave("./final_fig/overlaps/rep_overlap_cd8_tra_thy.eps", width = 9.5, height = 9.5, units = "cm")violin_tra_cd8 %>% filter(Organ1 == "LN" & Organ2 == "LN") %>%
mutate(organ_strain1 = paste(Organ1, Strain1),
organ_strain2 = paste(Organ2, Strain2)) %>%
mutate(comparison = paste(organ_strain1, organ_strain2, sep = " - ")) %>%
filter(comparison %in% c("LN WT - LN WT", "LN CA - LN WT", "LN CAKR - LN WT")) %>%
ggplot(aes(x = comparison, y = value)) +
geom_dotplot(binaxis='y', stackdir='center', dotsize=0) +
geom_jitter(position=position_jitter(0.2), size = 2, aes(color = comparison)) +
ggtitle("CD8 TRA LN") +
stat_summary(fun = "mean",
geom = "crossbar",
width = 0.5,
colour = "black") +
ylab("% repertoire overlap") +
theme_classic() +
ggtheme() +
theme(axis.text.x = element_text(angle = 90)) +
scale_color_manual(values = c("dodgerblue","indianred2","gray40")) +
ylim(c(0,0.35)) + xlab("")#ggsave("./final_fig/overlaps/rep_overlap_cd8_tra_LN.png", width = 8, height = 8, units = "cm")
#ggsave("./final_fig/overlaps/rep_overlap_cd8_tra_LN.eps", width = 8, height = 8, units = "cm")binary_trb <- excel_count_table_trb %>%
mutate_at(vars(starts_with("CD")), .funs = binary)
binary_trb_longer <- binary_trb %>% select(starts_with("CD"), aaSeqCDR3) %>% pivot_longer(!aaSeqCDR3, names_to = "num_id")
df_all4 <- data.frame("")
for(j in levels(factor(binary_trb_longer$num_id))){
subset1 <- binary_trb_longer %>% filter(num_id == j & value>0) %>% pull("aaSeqCDR3")
vector_overlap <- c()
for(i in levels(factor(binary_trb_longer$num_id))){
subset2 <- binary_trb_longer %>% filter(num_id == i & value>0) %>% pull("aaSeqCDR3")
intersect_rep <- length(intersect(subset1, subset2))
total <- length(subset2)
vector_overlap <- c(vector_overlap,intersect_rep/total)
}
df <- as.data.frame(x = vector_overlap)
colnames(df) <- j
df
df_all4 <- cbind(df_all4, df)
}
df_all4 <- df_all4[,2:29]
rownames(df_all4) <- colnames(df_all4)
#write.csv(df_all4, "rep_overlap_trb.csv")df24 <- df_all4
df24[df24 == 1] <- 0
### plot dotplot of overlaps
df25 <- df24 %>% rownames_to_column("id") %>% pivot_longer(-id)
g2 <- ggplot(df25, aes(id, factor(name, levels = rev(levels(factor(name)))))) +
geom_point(aes(size = value*100, colour = value*100)) +
theme_bw()
g2 + scale_size_continuous(range=c(7,12)) +
geom_text(aes(label = round(value*100, digits = 1))) +
scale_colour_gradient2(low = "lightskyblue", mid = "lightsteelblue2", high = "salmon") +
theme(axis.text.x = element_text(angle = 90)) rep_overlap_trb <- read_csv("rep_overlap_trb.csv") %>% column_to_rownames("...1")
rep_overlap_trb_cd4 <- rep_overlap_trb[1:14,1:14]
rep_overlap_trb_cd8 <- rep_overlap_trb[15:28,15:28]
rep_overlap_trb_cd4 <- rep_overlap_trb_cd4[rev(c(13,14,8:12,6,7,1:5)),rev(c(13,14,8:12,6,7,1:5))]
levels_names <- rev(colnames(rep_overlap_trb_cd4))
df_overlap_trb_cd4 <- rep_overlap_trb_cd4
df_overlap_trb_cd4[df_overlap_trb_cd4 == 1] <- 0
### plot dotplot of overlaps
df_overlap_trb_cd4 <- df_overlap_trb_cd4 %>% rownames_to_column("id") %>% pivot_longer(-id)
g2 <- ggplot(df_overlap_trb_cd4, aes(factor(name, levels = levels_names), factor(id, levels = rev(levels_names)))) +
geom_point(aes(size = value*100, colour = value*100)) +
theme_bw()
g2 + scale_size_continuous(range=c(5,9)) +
geom_text(aes(label = round(value*100, digits = 1)), size = 3) +
scale_colour_gradient2(low = "white", mid = "lightsteelblue", high = "salmon") +
theme(axis.text.x = element_text(angle = 90)) + ggtheme()#ggsave(file = "./final_fig/overlap_cd4_trb.png", width = 20, height = 16, units = "cm")
#ggsave(file = "./final_fig/overlap_cd4_trb.svg", width = 20, height = 16, units = "cm")rep_overlap_trb_cd8 <- rep_overlap_trb_cd8[rev(c(12:14,8:11,5:7,1:4)),rev(c(12:14,8:11,5:7,1:4))]
levels_names <- rev(colnames(rep_overlap_trb_cd8))
df_overlap_trb_cd8 <- rep_overlap_trb_cd8
df_overlap_trb_cd8[df_overlap_trb_cd8 == 1] <- 0
### plot dotplot of overlaps
df_overlap_trb_cd8 <- df_overlap_trb_cd8 %>% rownames_to_column("id") %>% pivot_longer(-id)
g2 <- ggplot(df_overlap_trb_cd8, aes(factor(name, levels = levels_names), factor(id, levels = rev(levels_names)))) +
geom_point(aes(size = value*100, colour = value*100)) +
theme_bw()
g2 + scale_size_continuous(range=c(5,9)) +
geom_text(aes(label = round(value*100, digits = 1)), size = 3) +
scale_colour_gradient2(low = "white", mid = "lightsteelblue", high = "salmon") +
theme(axis.text.x = element_text(angle = 90)) + ggtheme()#ggsave(file = "./final_fig/overlap_cd8_trb.png", width = 20, height = 16, units = "cm")
#ggsave(file = "./final_fig/overlap_cd8_trb.svg", width = 20, height = 16, units = "cm")violin_trb_cd4 <- rep_overlap_trb_cd4 %>%
rownames_to_column("sample1") %>%
pivot_longer(!sample1, names_to = "sample2") %>%
mutate(sample2 = stringr::str_replace_all(sample2, pattern = "Lymph nodes", replacement = "LN")) %>%
mutate(sample1 = stringr::str_replace_all(sample1, pattern = "Lymph nodes", replacement = "LN")) %>%
separate(sample1, into = c("Cell_type1","Organ1","Strain1","Exp1"), remove = F) %>%
separate(sample2, into = c("Cell_type2","Organ2","Strain2","Exp2"), remove = F) %>%
filter(value<1)
violin_trb_cd8 <- rep_overlap_trb_cd8 %>%
rownames_to_column("sample1") %>%
pivot_longer(!sample1, names_to = "sample2") %>%
mutate(sample2 = stringr::str_replace_all(sample2, pattern = "Lymph nodes", replacement = "LN")) %>%
mutate(sample1 = stringr::str_replace_all(sample1, pattern = "Lymph nodes", replacement = "LN")) %>%
separate(sample1, into = c("Cell_type1","Organ1","Strain1","Exp1"), remove = F) %>%
separate(sample2, into = c("Cell_type2","Organ2","Strain2","Exp2"), remove = F) %>%
filter(value<1)violin_trb_cd8 %>% filter(Organ1 == "Thymus" & Organ2 == "Thymus") %>%
mutate(organ_strbin1 = paste(Organ1, Strain1),
organ_strbin2 = paste(Organ2, Strain2)) %>%
mutate(comparison = paste(organ_strbin1, organ_strbin2, sep = " - ")) %>%
filter(comparison %in% c("Thymus WT - Thymus WT", "Thymus CA - Thymus WT", "Thymus CAKR - Thymus WT")) %>%
ggplot(aes(x = comparison, y = value)) +
geom_dotplot(binaxis='y', stackdir='center', dotsize=0) +
geom_jitter(position=position_jitter(0.2), size = 2, aes(color = comparison)) +
ggtitle("CD8 TRB Thymus") +
stat_summary(fun = "mean",
geom = "crossbar",
width = 0.5,
colour = "black") +
ylab("% repertoire overlap") +
theme_classic() +
ggtheme() +
theme(axis.text.x = element_text(angle = 90)) +
scale_color_manual(values = c("dodgerblue","indianred2","gray40")) +
ylim(c(0,0.25)) + xlab("")#ggsave("./final_fig/overlaps/rep_overlap_cd8_trb_thy.png", width = 9.5, height = 9.5, units = "cm")
#ggsave("./final_fig/overlaps/rep_overlap_cd8_trb_thy.eps", width = 9.5, height = 9.5, units = "cm")violin_trb_cd8 %>% filter(Organ1 == "LN" & Organ2 == "LN") %>%
mutate(organ_strbin1 = paste(Organ1, Strain1),
organ_strbin2 = paste(Organ2, Strain2)) %>%
mutate(comparison = paste(organ_strbin1, organ_strbin2, sep = " - ")) %>%
filter(comparison %in% c("LN WT - LN WT", "LN CA - LN WT", "LN CAKR - LN WT")) %>%
ggplot(aes(x = comparison, y = value)) +
geom_dotplot(binaxis='y', stackdir='center', dotsize=0) +
geom_jitter(position=position_jitter(0.2), size = 2, aes(color = comparison)) +
ggtitle("CD8 TRB LN") +
stat_summary(fun = "mean",
geom = "crossbar",
width = 0.5,
colour = "black") +
ylab("% repertoire overlap") +
theme_classic() +
ggtheme() +
theme(axis.text.x = element_text(angle = 90)) +
scale_color_manual(values = c("dodgerblue","indianred2","gray40")) +
ylim(c(0,0.25)) + xlab("")#ggsave("./final_fig/overlaps/rep_overlap_cd8_trb_LN.png", width = 8, height = 8, units = "cm")
#ggsave("./final_fig/overlaps/rep_overlap_cd8_trb_LN.eps", width = 8, height = 8, units = "cm")violin_trb_cd4 %>% filter(Organ1 == "Thymus" & Organ2 == "Thymus") %>%
mutate(organ_strbin1 = paste(Organ1, Strain1),
organ_strbin2 = paste(Organ2, Strain2)) %>%
mutate(comparison = paste(organ_strbin1, organ_strbin2, sep = " - ")) %>%
filter(comparison %in% c("Thymus WT - Thymus WT", "Thymus CA - Thymus WT", "Thymus CAKR - Thymus WT")) %>%
ggplot(aes(x = comparison, y = value)) +
geom_dotplot(binaxis='y', stackdir='center', dotsize=0) +
geom_jitter(position=position_jitter(0.2), size = 2, aes(color = comparison)) +
ggtitle("CD4 TRB Thymus") +
stat_summary(fun = "mean",
geom = "crossbar",
width = 0.5,
colour = "black") +
ylab("% repertoire overlap") +
theme_classic() +
ggtheme() +
theme(axis.text.x = element_text(angle = 90)) +
scale_color_manual(values = c("dodgerblue","indianred2","gray40")) +
ylim(c(0,0.20)) + xlab("")#ggsave("./final_fig/overlaps/rep_overlap_cd4_trb_thy.png", width = 9.5, height = 9.5, units = "cm")
#ggsave("./final_fig/overlaps/rep_overlap_cd4_trb_thy.eps", width = 9.5, height = 9.5, units = "cm")violin_trb_cd4 %>% filter(Organ1 == "LN" & Organ2 == "LN") %>%
mutate(organ_strbin1 = paste(Organ1, Strain1),
organ_strbin2 = paste(Organ2, Strain2)) %>%
mutate(comparison = paste(organ_strbin1, organ_strbin2, sep = " - ")) %>%
filter(comparison %in% c("LN WT - LN WT", "LN CA - LN WT", "LN CAKR - LN WT")) %>%
ggplot(aes(x = comparison, y = value)) +
geom_dotplot(binaxis='y', stackdir='center', dotsize=0) +
geom_jitter(position=position_jitter(0.2), size = 2, aes(color = comparison)) +
ggtitle("CD4 TRB LN") +
stat_summary(fun = "mean",
geom = "crossbar",
width = 0.5,
colour = "black") +
ylab("% repertoire overlap") +
theme_classic() +
ggtheme() +
theme(axis.text.x = element_text(angle = 90)) +
scale_color_manual(values = c("dodgerblue","indianred2","gray40")) +
ylim(c(0,0.20)) + xlab("")#ggsave("./final_fig/overlaps/rep_overlap_cd4_trb_LN.png", width = 8, height = 8, units = "cm")
#ggsave("./final_fig/overlaps/rep_overlap_cd4_trb_LN.eps", width = 8, height = 8, units = "cm")sessionInfo()## R version 4.2.1 (2022-06-23)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.5 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/liblapack.so.3
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] SmartEDA_0.3.8 kableExtra_1.3.4 factoextra_1.0.7 pheatmap_1.0.12
## [5] immunarch_0.8.0 data.table_1.14.2 dtplyr_1.2.2 patchwork_1.1.2
## [9] cowplot_1.1.1 readxl_1.4.1 forcats_0.5.2 stringr_1.4.1
## [13] dplyr_1.0.10 purrr_0.3.4 readr_2.1.2 tidyr_1.2.1
## [17] tibble_3.1.8 ggplot2_3.3.6 tidyverse_1.3.2
##
## loaded via a namespace (and not attached):
## [1] uuid_1.1-0 backports_1.4.1 circlize_0.4.15
## [4] fastmatch_1.1-3 systemfonts_1.0.4 plyr_1.8.7
## [7] igraph_1.3.5 digest_0.6.29 foreach_1.5.2
## [10] htmltools_0.5.3 viridis_0.6.2 ggalluvial_0.12.3
## [13] fansi_1.0.3 magrittr_2.0.3 googlesheets4_1.0.1
## [16] cluster_2.1.4 doParallel_1.0.17 tzdb_0.3.0
## [19] graphlayouts_0.8.1 modelr_0.1.9 vroom_1.5.7
## [22] svglite_2.1.0 lpSolve_5.6.15 rmdformats_1.0.4
## [25] colorspace_2.0-3 rvest_1.0.3 ggrepel_0.9.1
## [28] haven_2.5.1 xfun_0.33 crayon_1.5.2
## [31] jsonlite_1.8.1 phangorn_2.10.0 iterators_1.0.14
## [34] ape_5.6-2 glue_1.6.2 polyclip_1.10-0
## [37] gtable_0.3.1 gargle_1.2.1 webshot_0.5.3
## [40] UpSetR_1.4.0 car_3.1-0 kernlab_0.9-31
## [43] shape_1.4.6 ISLR_1.4 prabclus_2.3-2
## [46] DEoptimR_1.0-11 abind_1.4-5 scales_1.2.1
## [49] GGally_2.1.2 DBI_1.1.3 rstatix_0.7.0
## [52] Rcpp_1.0.9 viridisLite_0.4.1 xtable_1.8-4
## [55] bit_4.0.4 mclust_5.4.10 stats4_4.2.1
## [58] sampling_2.9 httr_1.4.4 RColorBrewer_1.1-3
## [61] fpc_2.2-9 modeltools_0.2-23 ellipsis_0.3.2
## [64] reshape_0.8.9 pkgconfig_2.0.3 flexmix_2.3-18
## [67] farver_2.1.1 nnet_7.3-17 sass_0.4.2
## [70] ggseqlogo_0.1 dbplyr_2.2.1 utf8_1.2.2
## [73] labeling_0.4.2 tidyselect_1.1.2 rlang_1.0.6
## [76] reshape2_1.4.4 later_1.3.0 munsell_0.5.0
## [79] cellranger_1.1.0 tools_4.2.1 cachem_1.0.6
## [82] cli_3.4.1 generics_0.1.3 broom_1.0.1
## [85] evaluate_0.16 fastmap_1.1.0 yaml_2.3.5
## [88] bit64_4.0.5 knitr_1.40 fs_1.5.2
## [91] tidygraph_1.2.2 robustbase_0.95-0 ggraph_2.0.6
## [94] nlme_3.1-159 mime_0.12 xml2_1.3.3
## [97] compiler_4.2.1 shinythemes_1.2.0 rstudioapi_0.14
## [100] ggsignif_0.6.3 reprex_2.0.2 tweenr_2.0.2
## [103] bslib_0.4.0 stringi_1.7.8 highr_0.9
## [106] lattice_0.20-45 Matrix_1.5-1 vctrs_0.4.2
## [109] stringdist_0.9.8 pillar_1.8.1 lifecycle_1.0.2
## [112] jquerylib_0.1.4 GlobalOptions_0.1.2 httpuv_1.6.6
## [115] R6_2.5.1 bookdown_0.28 promises_1.2.0.1
## [118] gridExtra_2.3 codetools_0.2-18 MASS_7.3-58.1
## [121] assertthat_0.2.1 withr_2.5.0 rlist_0.4.6.2
## [124] diptest_0.76-0 parallel_4.2.1 hms_1.1.2
## [127] quadprog_1.5-8 grid_4.2.1 class_7.3-20
## [130] rmarkdown_2.16 carData_3.0-5 googledrive_2.0.0
## [133] ggpubr_0.4.0 ggforce_0.4.0 shiny_1.7.2
## [136] lubridate_1.8.0